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The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method's superiority over conventional moving averages and ramp-rate filters. [ABSTRACT FROM AUTHOR]

This paper provides a thorough exploration of the evolution and contemporary trends in electrical-distribution networks, with a focus on smart grids in the context of Industry 4.0. Beginning with the traditional components of electrical grids, the study highlights the transition towards sustainable energy sources and the integration of renewables. Key trends include economic operation, the application of distributed energy resources, and the significance of photovoltaic solar energy. The paper unfolds in seven sections, examining smart-electrical-network architecture, sustainable technology progression, energy efficiency, carbon-emission-reduction challenges, future perspectives, and concluding insights. Each section delves into specific layers and aspects, such as data management, electrical infrastructure, automation, and consumer interaction. The intricate role of smart meters and their impact on energy management is explored, providing a comprehensive overview of the current state and future directions of electrical-distribution networks. [ABSTRACT FROM AUTHOR]

In the current study, a fixed‐bed downdraft gasifier was used, in addition to gas cleaning and treatment units, to evaluate hydrogen production from the waste from a date molasses factory. Date pits and pomace were gasified at a temperature of 800°C with oxygen‐rich air and standard air as the gasifying agents. Simulations were performed using the Cycle‐Tempo simulation tool. The impact of many process parameters (air–fuel ratio, gasification temperature, moisture content, oxygen concentration and temperature of the water–gas shift) on gas composition was investigated. The economic evaluation of hydrogen production is also discussed in this paper. Depending on the operating conditions, the results indicate that gasification of biomass in oxygen‐rich air improves hydrogen yield compared with air gasification of biomass. Over the range of operating conditions examined, the maximum hydrogen production reached 45.86% for pits and 35.81% for pomace from gasification with oxygen‐rich air while the H2 content reached 36.71 and 28.72% respectively for air gasification of pits and pomace. The cost analysis for the date waste gasification unit showed that the hydrogen production cost is $3/kg for date pits and $4/kg for pomace. The production of hydrogen from date molasses manufacturing waste helps to lessen reliance on fossil fuels and is an environmentally beneficial alternative source of renewable energy. [ABSTRACT FROM AUTHOR]

The reliance on imported fuels for electricity generation and internal transportation in insular electrical systems has historically posed a significant challenge due to their geographic isolation. The vulnerability of insular ecosystems to pollution has driven the need to transition toward renewable energy sources. Despite their inherent variability, wind and solar energy have gained acceptance. Integrating these renewable technologies into insular grids presents technical challenges that impact the quality of the power supply, particularly with the increasing presence of electric vehicles. Nevertheless, the batteries of these vehicles provide an opportunity to enhance network performance. This article introduces an innovative power smoothing technique that utilizes electric vehicle batteries to optimize self-consumption and reduce power fluctuations. The proposed method is an enhanced version of the ramp-rate energy smoothing method, incorporating adaptability through real-time control of the ramp-rate using fuzzy logic. It employs an aggregated model of lithium-ion batteries with a bidirectional power electronic converter. Experimental validation is carried out in the Micro-Grid Laboratory of the University of Cuenca, Ecuador. Experimental results demonstrate a significant 14% reduction in energy generation variability, resulting in a more stable electrical supply profile. Additionally, there is a marginal improvement in energy delivery, with an additional injection of 0.23 kWh compared to scenarios without the participation of electric vehicle batteries in power smoothing tasks. These findings support the effectiveness of the proposed approach in optimizing the integration of intermittent renewable generators and electric vehicle charging in insular energy systems. [ABSTRACT FROM AUTHOR]

The integration of renewable resources in isolated systems can produce instability in the electrical grid due to its intermintency. In today's microgrids, which lack synchronous generation, physical inertia is substituted for inertia emulation. To date, the most effective approach remains the frequency derivative control technique. Nevertheless, within this method, the ability to provide virtual drooping is often disregarded in its design, potentially leading to inadequate development in systems featuring high renewable penetration and low damping. To address this issue, this paper introduces an innovative design and analysis of virtual inertia control to simultaneously mimic droop and inertia characteristics in microgrids. The dynamic frequency response without and with renewable energy sources penetration is comparatively analyzed by simulation. The proposed virtual inertia control employs a derivative technique to measure the rate of change of frequency slope during inertia emulation. Sensitivity mapping is conducted to scrutinize its impact on dynamic frequency response. Finally, the physical battery storage system of the University of Cuenca microgrid is used as a case study under operating conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case. [ABSTRACT FROM AUTHOR]

This study presents a 100% renewable and diversified system taking advantage of the available energy potential of renewable energies in Mexico with a view to a planned energy transition in cooperation with the environment. The processes of change that are experienced worldwide in favor of the planet make us reflect and propose alternatives that break traditional schemes in the production of energy (for which reason Mexico cannot deviate from its current model). It is here that this research becomes a transcendental and important reference for decision-making and the transformation of the energy sector in Mexico. The current electrical system relies on fossil fuels that need to be replaced by renewable energy sources (and it is necessary to satisfy growing demands in the long term). The methodological process is carried out with the use of the 100% renewable energy market design tool EnergyPLAN, which puts the concept of intelligent energy into practice by 2050. Finally, after analyzing the results, it is concluded that a good energy mix for 2050 is 30% solar photovoltaic, 25% wind, 14.5% hydraulic, 13.8% CSP plants, and 16.7% other technologies. Surpluses may be sold to the United States and Central America through interconnection points. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient Gorilla troops-inspired algorithm to cope optimal power flow (OPF) problem considering uncertainty of renewable energy sources (RES). The problem is formulated as large-scale constrained optimization problem with non-linear characteristics. Its degree of complexity increases with incorporation of intermittent energy sources, making it harder to be solved using conventional optimization techniques. However, could be efficiently resolved by nature-inspired optimization algorithms and solvers. The objective function is the overall cost of system, including reserve cost for over-estimation and penalty cost for under-estimation of two types of PV-solar and wind energy. To demonstrate the consistency and robustness of the developed algorithm a case study on the modified IEEE 30-bus system and and Adrar's power network (isolated grid) is carried out. Simulation results show the capability of GTO to find high quality optimal feasible solutions and ranked first among the compared algorithms, and so, over different function landscapes. [ABSTRACT FROM AUTHOR]

This study addresses the challenging task of solving optimal reactive power dispatch (ORPD) while incorporating renewable energy resources (RERs), considering their stochastic and time‐varying nature. Specifically, the focus is on solving the stochastic optimal reactive power dispatch (SORPD) problem, taking into account uncertainties in load demand and generated power, as well as the reactive power generation capability of photovoltaic (PV) systems. To tackle this problem, an enhanced Artificial Gorilla Troops Optimizer (EGTO) is proposed, which utilizes multiple strategies. The objective is to minimize power loss and improve voltage profile and system stability under uncertain conditions. The algorithm is applied and tested on the IEEE 30‐bus system, both with and without the STATCOM functionality of the PV system. A comparison is conducted against other well‐known optimization algorithms. The results demonstrate the significant improvement achieved by incorporating the PV unit. The inclusion of the PV system leads to reduced expected power losses, voltage deviations, and improved voltage stability. Specifically, without and with the STATCOM functionality, the expected power loss decreases from 5.9218 MW at the base case to 1.1419 MW and 1.1197 MW, respectively. Similarly, the expected voltage deviation decreases from 1.9320 p.u. to 0.0909 p.u. and 0.0893 p.u., respectively, and the expected voltage stability decreases from 0.1336 p.u. to 0.07199 p.u. and 0.07142 p.u., respectively. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy. [ABSTRACT FROM AUTHOR]

As the world's population continues to grow and the demand for energy increases, there is an urgent need for sustainable and efficient energy systems. Renewable energy sources, such as wind and solar power, have the potential to play a significant role in meeting this demand, but their intermittency can make integration into existing energy systems a challenge. Moreover, the development of sustainable energy systems has become even more critical in recent years, due to a confluence of events, including the decline in fuel prices, geopolitical conflicts, and the recent COVID-19 pandemic. The decrease in fuel prices has led to a decline in investment in renewable energy and has slowed the transition to sustainable energy systems. Additionally, geopolitical conflicts and pandemics have highlighted the need for resilient and self-sufficient energy systems that can operate independently of external factors. Also, energy storage technologies play a critical role in achieving this goal by providing reliable backup power and enabling microgrids to operate independently of the larger power grid. As such, developing efficient and effective energy storage technologies is essential for creating sustainable energy systems that can meet the demands of modern society while mitigating the impact of external factors. In this regard, this work provides an overview of microgrids' latest energy storage technologies, including their applications, types, integration strategies, optimization algorithms, software, and uncertainty analysis. Energy storage technologies have a wide range of applications in microgrids, including providing backup power and balancing the supply and demand of energy. Different energy storage techniques have been discussed, including batteries, flywheels, supercapacitors, pumped hydro energy storage, and others. Moreover, integration strategies of energy storage in microgrids, models, assessment indices, and optimization algorithms used in the design of energy storage systems are presented in detail. The capabilities of software used in energy storage sizing are explored. Further, uncertainty analysis in modeling energy storage devices is presented and discussed. This state-of-the-art technology has been prepared to demonstrate the effectiveness of energy storage technologies in microgrids, providing valuable insights for future developments in the field. [ABSTRACT FROM AUTHOR]

The constant changes in the load power lead permanently to a power mismatch between the power generation and the power consumption. So, the system frequency due to the power imbalance deviates from the nominal value. Consequently, a control loop should be implemented to stabilize the system frequency whenever a load change occurs. This paper presents a new super‐twisting sliding mode control methodology for obtaining an optimal frequency performance in a multi‐pool system. The paper presents a three‐pool system for frequency deviation problems using an optimal gain Super Twisting Sliding Mode Controller (STSMC), which regulates the frequency change and the line power change to zero in a minimal time. The extent of the excellence of the study proposed is evaluated by comparing it with three Benchmark classical controllers, which are the Tilt‐Integral‐Derivative (TID), Proportional‐Integral‐Derivative (PID), and Fractional‐Order PID (FOPID). The parameters of the four controllers are determined by a proposed physical meta‐heuristic optimization technique called Transient Search Optimizer (TSO), inspired by the dynamic behaviour in the electrical circuits comprising storage elements such as capacitors and inductors during the switching actions. The system simulation is performed, and the STSMC proved overwhelming superiority over other controllers, as it deals better with the transient interval of the system response. Renewable Energy sources (RESs) like photovoltaic and wind energy systems are established, and the STSMC is tested with industrial and residential load models. Finally, energy storage devices such as batteries and superconducting magnetic energy storage are implemented to suppress the rapid fluctuations in the system response, and it succeeded in doing that as the system oscillations are greatly damped with the energy storage devices. [ABSTRACT FROM AUTHOR]

Hybrid Renewable Energy Systems have been considered as a suitable way to supply electricity. The hybrid energy (Solar-Wind-Storage-hydrogen-diesel) is increasingly used in various applications, especially at isolated sites. This study proposes the optimization and analysis of a stand-alone photovoltaic/battery/fuel cell/electrolyzer/hydrogen hybrid system (HS) to find out the optimal sizes and costs. In the first step, SAM software was used to estimate the global solar irradiance (GHI) for the studied area and to select the best PV, battery and fuel cell components. MATLAB software was handled to predict the power output of the renewable source (PV system) using an Artificial Neural Network (ANN) model. Then, simulation, optimization and techno-economic analysis were carried out in the second step with HomerPro software. Four proposed configurations are optimized in this work and different comparisons have been made regarding the found results. The results of the cost optimization were found to be 50861.21 $ and 0.95657 $ for NCP and COE, respectively. Moreover, the same HS configurations are simulated and optimized under Moroccan climate. • Hybrid Renewable Energy Systems is a suitable way to supply electricity. • A stand-alone photovoltaic/battery/fuel cell/electrolyzer/hydrogen hybrid system. • SAM software was used to estimate the global solar irradiance. • MATLAB was handled using an Artificial Neural Network model. • Optimization and techno-economic analysis with HomerPro software. [ABSTRACT FROM AUTHOR]

Nowadays, energy decarbonization due to integrating renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical system networks, especially in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method for improving the low-pass filter and moving average for grid-connected photovoltaic systems. This novel method includes state-of-charge monitoring control of the supercapacitor's energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. This research aims to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration. The results demonstrate significant power quality and stability improvements achieved through the proposed method. For instance, the power smoothing method effectively reduced power fluctuations by 16.7% with the low-pass filter, 14.05% with the ramp-rate filter, and 9.7% with the moving average filter. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a new, metaheuristic optimization technique, Artificial Gorilla Troops Optimization (GTO), for a hybrid power system with photovoltaic (PV) and wind energy (WE) sources, solving the probabilistic optimum power flow (POPF) issue. First, the selected algorithm is developed and evaluated such that it applies to solve the classical optimum power flow (OPF) approach with the total fuel cost as the objective function. Second, the proposed algorithm is used for solving the POPF, including the PV and WE sources, considering the uncertainty of these renewable energy sources (RESs). The performance of the suggested algorithm was confirmed using the standard test systems IEEE 30-bus and 118-bus. Different scenarios involving different sets of the PV and WE sources and fixed and variable loads were considered in this study. The comparison of the obtained results from the suggested algorithm with other algorithms mentioned in this literature has confirmed the efficiency and performance of the proposed algorithm for providing optimal solutions for a hybrid power system. Furthermore, the results showed that the penetration of the PV and WE sources in the system significantly reduces the total cost of the system. [ABSTRACT FROM AUTHOR]

Recently, renewable energy sources have become an encouraging solution to the availability and environmental problems arising from using fossil fuels for energy production. The article's goal is to provide a reliable and doable path toward producing electricity from biomass by using palm waste (fronds, empty fruit bunches, and trunks). The system is simulated using Cycle-Tempo software. Waste was gasified at 800°C using a downdraft fixed-bed reactor to produce syngas. After that, a producer gas flow of around is sent to an externally fired gas turbine (EFGT), where it is promptly burned at atmospheric pressure to produce electricity. According to the simulation's outcomes, palm fronds are the waste that worked best as a feedstock, and an estimated 390 kW of electric power could be generated by the externally fired gas turbine with a 19% electrical efficiency. The producer gas had a good molecular composition (H2 15.02%, CO 18.1%, CH4 1.25%, CO2 10.79%, H2O 8.17%, and N2 46.08%), a low heating value of up to (4.16 MJ/kg), a gasification efficiency of 77%, and a CHP efficiency of 61%. A comparison of the findings with previously published literature revealed that the proposed technology allows for the production of syngas with a satisfactory composition from palm fronds. [ABSTRACT FROM AUTHOR]

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG. [ABSTRACT FROM AUTHOR]

Storage systems and demand-response programs will play a vital role in future energy systems. Batteries, hydrogen or pumped hydro storage systems can be combined to form hybrid storage facilities to not only manage the intermittent behavior of renewable sources, but also to store surplus renewable energy in a practice known as 'green' storage. On the other hand, demand-response programs are devoted to encouraging a more active participation of consumers by pursuing a more efficient operation of the system. In this context, proper scheduling tools able to coordinate different storage systems and demand-response programs are essential. This paper presents a stochastic mixed-integer-lineal-logical framework for optimal scheduling of isolated microgrids. In contrast to other works, the present model includes a logical-based formulation to explicitly coordinate batteries and pumped hydro storage units. A case study on a benchmark isolated microgrid serves to validate the developed optimization model and analyze the effect of applying demand-response premises in microgrid operation. The results demonstrate the usefulness of the developed method, and it is found that operation cost and fuel consumption can be reduced by ~38% and ~82% by applying demand-response initiatives. [ABSTRACT FROM AUTHOR]

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system's demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study's main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators' operation, due to the penetration of renewable energy sources into the studied networks. [ABSTRACT FROM AUTHOR]

Backward/forward load flow algorithm is commonly used in the power system, particularly in radial distribution networks. However, the rapid development in the power electronics technologies makes power systems more flexible with the integration of Renewable Energy Resources (RESs) and compensation devices. Consequently, this paper presents simple models for wind turbine generators based on Squirrel Cage Induction Generator (SCIG) and Distribution Static Synchronous Compensator (DSTATCOM) into a backward/forward load flow algorithm. In the wind generator model, the mechanical powers are collected from the manufactures data where the electrical power is calculated through the SCIG equivalent circuit and injected as negative loads at the connected buses. The second model in this paper is the DSTATCOM devices which are utilized to recover the SCIG reactive power. In the proposed model, the connected DSTATCOM bus is converted to a voltage-controlled bus (PV-bus type) with zero active power. Moreover, the reactive power of the DSTATCOM is updated during the iterative process until the load flow convergence. The simplified models for both wind generators and DSTATCOM are validated based on standard IEEE 33-bus and 85-bus radial distribution systems. The obtained results show the feasibility and effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, microgrids are emerging as an invaluable framework for the integration of renewable energy sources and demand response programs. In such systems, energy storage facilities are also frequently deployed to properly manage surplus energy from renewable sources on pursuing more efficient management of the system. Hybrid storage systems in which various storage facilities are combined may result in a more effective solution than only considering one storage technology. This way, the good features of the different technologies may be jointly exploited while their drawbacks are minimized. Due to the large‐scale integration of renewable energies in this kind of grid, coping with uncertainties becomes a critical issue. Moreover, the operation of microgrids frequently deals with other kinds of uncertainties related to energy pricing from the upscale grid (in the case of grid‐connected mode) or local demand. This way, proper modeling of uncertainties is essential for adequately operating these systems. This paper contributes to this pool by developing a novel interval‐based formulation, for optimal scheduling of microgrids considering battery and pumped‐hydro storage systems. To achieve this goal, the optimal scheduling of a microgrid with pumped‐hydro and battery energy storage considering demand response is modeled, firstly. Then, the new interval‐based formulation is used to cope with the uncertainties. Finally, the suggested model is verified using simulations in various cases, and the results confirm the effectiveness of the novel interval‐based formulation for the optimal scheduling of microgrid with pumped‐hydro and battery energy storage under uncertainty. [ABSTRACT FROM AUTHOR]

This research presents a completely decarbonized system structured with renewable energies for the fragile ecosystem of the Galapagos Islands of Ecuador, with a horizon to the year 2050. The transition process begins with clear legal actions and allocating economic resources that clearly promote a change in the energy matrix and the transition would be achieved until the year 2050. The Galapagos Islands are the second largest marine reserve on the planet, declared a World Heritage Site in 1978 for UNESCO. The archipelago, a beautiful ecological attraction, has tourism as its main economic source. The biggest problem is in the supply of energy that mainly comes from fossil fuels, in addition to that, the smell of fuel in several places is out of tune with the harmonious nature and its species. The efforts began to be noticed when in the Archipelago the new implementations of renewable energy began to become a reality with the purpose of stopping using fossil fuels. In this sense, there is the potential to take advantage of the resources available in specific sites, especially the photovoltaic and wind solar considered in the National Plan All a Life. The change in the productive matrix from the national level also implies a change in demand, including at the transportation level with the use of electric vehicles and the use of electric stoves in homes. The purpose is that energy services in the Galapagos Islands progressively increase, on the one hand, replacing fossil fuels and on the other, supplying the growing demand with a view to 2050, taking advantage of renewable energies until reaching 100%. • The scenario outlined is 100% renewable energy by 2050 in the Galapagos Islands. •The Galapagos archipelago has great renewable energy potential. •The decarbonization in Galapagos will have a positive impact on the socioeconomic and environmental aspects. •Decarbonization is feasible by 2050 depending on the economic situation. •The electricity system of Galapagos will allow it to maintain its status as a Cultural Heritage of Humanity. [ABSTRACT FROM AUTHOR]

The article considers feasibility of using air-water heat pumps to provide residential and industrial buildings with heating, cooling and ventilation requires taking into account a large number of different factors, such as the estimated and average outdoor temperature for the heating period in the considered climate region. The study of temperature fluctuations during the heating period will help to clarify the boundaries of areas of synchronous changes to solve the problems of climatological zoning, with the goal of establishing the heat production, energy transformation coefficient of the selected heat pump model. Using a statistical data and quantitative indicators of temperature variability across the territory of Kazakhstan, an interactive online map was developed based on the ArcGIS software, which integrated 67 weather stations in five regions. The developed zoning map is a qualitatively new information product that combines reliable data and a tool for analysis, visualization and data management of a professional GIS system. In general, in conclusion, we can say that our work is the creation of a map of the zoning of the territory of Kazakhstan according to the average temperature of the heating period in order to select a heat pump heat supply system is relevant in that we take into account the climatic conditions and the heating season to select a heat transfer system. [ABSTRACT FROM AUTHOR]

Load growth in electrical distribution networks became naturally occurring due to industrial development and human population demand growth. Consequently, system losses are continually raised while the voltage profile is reduced. This paper presents a novel hybrid method to determine the best locations and sizes of single and multiple of different renewable distributed energy resources (DER). The presented hybrid method is based on Salp Swarm Algorithm (SSA) and combined power loss sensitivity (CPLS). Integration of photovoltaics (PV) and wind turbines (WT) in distribution network is used to enhance the system voltage, minimize system losses and increase the system capacity. The effect of annual load growth in system load and system operating constraints are taken in consideration. IEEE 33-bus and 69-bus radial distribution systems (RDS) are used to validate the presented algorithm for integrating the DERS in distribution system. In addition, the presented algorithm is compared with different recent optimization algorithms in order to prove its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

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Global environmental problems associated with traditional energy generation have led to a rapid increase in the use of renewable energy sources (RES) in power systems. The integration of renewable energy technologies is commercially available nowadays, and the most common of such RES technology is photovoltaic (PV). This paper proposes an application of hybrid teaching-learning and artificial bee colony (TLABC) technique for determining the optimal allocation of PV based distributed generation (DG) and battery energy storage (BES) units in the distribution system (DS) with the aim of minimizing the total power losses. Besides, some potential nodes identified by the power loss sensitivity factor (PLSF). Thereupon TLABC is applied to determine the location of the DG and its size from the candidate nodes. The beta probability distribution function (PDF) is employed to characterize the randomness of solar radiation. High penetration of RES can lead to a high level of risk in DS stability. To maintain system stability, BES is considered to smooth out the fluctuations and improve supply continuity. The benefits of using BES is mainly dependent on operational strategies related to PV and storage in DS. The performance of the developed approach is tested on the 69 node and 118 node DSs and compared with the differential evolution (DE) algorithm, genetic algorithm (GA), for a fair comparison. Besides, the developed approach compared with other methods in literature which are solved the same problem. The results show how practical is the developed approach compared with other techniques. [ABSTRACT FROM AUTHOR]

• Developing a three-level mechanism for FOEM of multi- RBMGS. • Computing the required flexibility of the main grid considering RBMGs programming. • Decentralized programming of IESHs and PEVs, taking into account the residents' SI. • Reducing the daily costs by extracting the flexibility from RBMG resources. • Increasing day-ahead programming safety by implementing a risk-averse strategy. A multi-stage mechanism for flexibility-oriented energy management (FOEM) of the distribution system is developed in this article, which main novelty is providing the flexibility requirements of the main grid by resources within renewable-based microgrids (RBMGs), including distributed generations (DGs), storage systems (SDs), internet-of-things enabled smart homes (IESHs), and plug-in electric vehicles (PEVs). In the first stage, IESHs are programmed in a decentralized space subject to satisfying the residents' satisfaction index (SI). In the second stage, RBMGs are programmed according to the announced plan of IESHs. Ultimately, in the third stage, the main grid operator calculates the required flexibility capacities of the system according to the announced plans of RBMGs, where a risk-averse strategy is adopted to manage the risk of programming. Next, the main grid operator announces the required flexibility capacities to RBMGs and IESHs so that they can re-plan with the possibility of participating in flexibility markets. The numerical results clarify that the participation of RBMGs, IESHs, and plug-in electric vehicles (PEVs) in providing flexibility capacities not only reduces the dependence of the distribution system on the upstream grid (UG), but also reduces the total daily costs by 30.7%. [ABSTRACT FROM AUTHOR]

Recently, fast uptake of renewable energy sources (RES) in the world has introduced new difficulties and challenges; one of the most important challenges is providing economic energy with high efficiency and good quality. To reach this goal, many traditional and smart algorithms have been proposed and demonstrated their feasibility in obtaining the optimal solution. Therefore, this paper introduces an improved version of Bonobo Optimizer (BO) based on a quasi-oppositional method to solve the problem of designing a hybrid microgrid system including RES (photovoltaic (PV) panels, wind turbines (WT), and batteries) with diesel generators. A comparison between traditional BO, the Quasi-Oppositional BO (QOBO), and other optimization techniques called Harris Hawks Optimization (HHO), Artificial Electric Field Algorithm (AEFA) and Invasive Weed Optimization (IWO) is carried out to check the efficiency of the proposed QOBO. The QOBO is applied to a stand-alone hybrid microgrid system located in Aswan, Egypt. The results show the effectiveness of the QOBO algorithm to solve the optimal economic design problem for hybrid microgrid power systems. [ABSTRACT FROM AUTHOR]

Recently, power systems are confronting a lot of challenges. Increasing the dependence on renewable energy sources especially wind energy and its impact on the stability of electrical systems are the most important challenges. Flexible alternating current transmission systems (FACTS) can be used to improve the relationship between wind farms and electrical grids. The performance of these FACTS depends on the parameters of its control system. These parameters can be tuned using modern methods like Artificial Neural Network (ANN). In this paper, ANN is used to improve the performance of static synchronous series compensator (SSSC) integrated into combined wind farm (CWF). This CWF is composed of squirrel cage induction generators (SCIG) and doubly fed induction generators (DFIG) wind turbines. This wind farm is collecting the advantage of SCIG and DFIG wind turbines. To view out the motivation of this paper, a comparison is done among the performances of combined wind farm (CWF) with ANN-SSSC, CWF with ordinary SSSC and CWF with SSSC tune by Multiobjective genetic algorithm (MOGA SSSC). The root mean square Error (RMSE) is used to evaluate the results. The results illustrate that the performance of CWF can be improved using SSSC adjusted by ANN. [ABSTRACT FROM AUTHOR]

Communication latency which inherently occurs in wide area measurement system greatly degrades small-signal stability (SSS) and is stochastic in nature, and thus, the current power oscillation damping controllers (PODCs) designed to improve SSS must consider this crucial factor. This paper proposes a probabilistic method to tune the parameters of PODCs incorporated in renewable farms to improve SSS under stochastic time delay and under other power system uncertainties arising due to renewable energy resources and loads. The proposed method is composed of two stages: The first stage quantifies the effect of time delay and other power system uncertainties on SSS, and the second stage uses this information to formulate an optimization problem. This optimization problem is solved with the help of four swarm intelligence-based optimization algorithms which are: bat algorithm, cuckoo search algorithm, firefly algorithm, and particle swarm optimization algorithm. The solutions from all these four optimization algorithms are compared, and the best result is used to optimize the parameters of the PODCs. All the analyses were conducted on a modified IEEE 68 bus system. The results show that the PODCs tuned using the proposed method greatly enhances the SSS margin under different scenarios and are probabilistically robust to the varying time delay and other power system uncertainties. [ABSTRACT FROM AUTHOR]

The integration of several renewable energy sources (RESs), such as photovoltaic generation (PVG) and wind turbine generation (WTG), introduces a significant amount of uncertainty for the optimal planning of the electrical power systems. The optimal power flow (OPF) problem became more difficult to solve when RESs uncertainties were taken into account. In this research, the stochastic OPF problem is solved using an Adaptive Lightning Attachment Procedure Optimizer (ALAPO) with the integration of RESs. The ALAPO is proposed in this study to improve both the exploration and exploitation process of the conventional Lightning Attachment Procedure Optimizer (LAPO). In order to create a number of scenarios that account for the uncertainties associated with load demand, wind speed, and solar irradiation, the scenario based reduction method is utilized. Further, to determine the optimal setting of active power of generators, tap setting of the transformers, reactive power of the capacitor banks, and the voltage profile at the generator buses for each scenario of RESs the objective function considered in this study is to minimize the power loss, voltage deviation, and voltage stability index of the power system. The effectiveness of the proposed algorithm is tested on IEEE 57-bus system. The results in this study show that the proposed ALAPO is superior for solving the OPF with the integration of RESs compared to the conventional methods like LAPO, Particle Swarm Optimization (PSO), Sand Cat Swarm Optimizer (SCSO), Gray Wolf Optimizer (GOW), Whale Optimization Algorithm (WOA), Black Widow Optimization Algorithm (BWO), and Capuchin Search Algorithm (CapSA). [Display omitted] • Integration of several renewable energy. • Optimal power flow (OPF) problem. • Adaptive Lightning Attachment Procedure Optimizer (ALAPO). • Voltage profile at the generator buses is the objective function considered. [ABSTRACT FROM AUTHOR]

• Real case studies of Tazouta and Fez rural area in Morocco. • Hybrid biomass, wind, and solar electricity generation in areas those are rich in renewable energies. • Potential of bioenergy for producing power in rural areas of Fez-Meknes region in Morocco. High dependence of Morocco's energy sector on imported fossil fuels and subsequent associated expensive import bills, as well as global agreements with greenhouse gas emission reduction, has motivated Morocco to utilize renewable energy sources such as hydro, wind, and solar for energy generation. However, in recent years, the use of wind and solar energies in Morocco has accelerated more than hydropower usage because of the reduction in water levels as a result of rainfall drop, global warming, and natural droughts. Morocco has a great potential for electricity generation from solar and wind energies because of many sunny days, vast desert areas, long coastlines, and appropriate wind blow. However, accommodations of wind and solar generating units in electric power systems are difficult because of wind speed variations, solar irradiation uncertainty, and costly energy storage systems. Regarding the huge amounts of biowaste in Morocco, power generation from biomass can resolve this problem and compensate for the decrease in wind and solar energy generation during cloudy, rainy, and/or non-windy moments. Therefore, in the present paper, the capability of hybrid electric energy generation, using biomass, wind, and solar in a Moroccan village of Tazouta and the rural area of Fez city is evaluated. The proposed mixed-integer linear programming model was implemented in A Mathematical Programming Language (AMPL) using a linear solver of CPLEX. The biomass, solar, and wind power plants are biogas units fed by cow manure, 1-kW photovoltaic (PV) modules, and 5.1-kW wind turbines, respectively. The evaluation of the results indicates that hybrid utilization of wind, PV, and biomass energy resources is a more appropriate way for electricity generation in Tazouta and Fez rural areas compared to the individual operation of wind turbines and other combinations of mentioned renewable energy sources. [ABSTRACT FROM AUTHOR]

Copyright of Problems of Sustainable Development / Problemy Ekorozwoju is the property of Faculty of Environmental Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Day-ahead scheduling has been carried out for energy hub. • Uncertainties of demands, PV, solar heat, wind and electricity prices are considered. • Demand response aggregator has been used as an input source of electricity. • Demand response effect on EH operation cost has been investigated. • Effect of storage systems on hub operation costs have been evaluated. Energy Hubs (EHs) play an important role in sustainable cities; they are multi-carrier energy systems that can satisfy different energy needs of consumers by relying on the conversion and storage of energy sources as well as renewable energy sources. With efficient and reliable energy supply, EHs may significantly contribute in developments of sustainable cities. In this paper, day-ahead scheduling of EHs is done, while they are connected to demand response aggregators. The studied EH includes photovoltaic and wind renewable sources, biomass, hydrogen electrolyzer, combined heat and power unit, solar heater, boiler, electric, thermal and hydrogen storage systems. Besides electric grid and gas network as input sources, EH may purchase electricity from demand response aggregators. Information gap decision theory (IGDT) is employed as a risk-aware method to handle uncertainties of electric, thermal and hydrogen demands, photovoltaic and wind power, solar heat and electricity prices. The scheduling is carried out from the perspective of the uncertainty free, risk-averse and risk- seeking decision-makers. The problem is formulated as a mixed-integer model and is solved using CPLEX solver in General algebraic modeling system (GAMS). The impact of risk awareness and deviation factors of critical and target costs on day-ahead scheduling and EH operation costs is investigated. The results show that the transaction with demand response aggregator decreases EH operation cost by 20.1%. The results also show that electric, thermal and hydron storage systems respectively decrease the operation cost by 3, 1.7 and 2.1%. [ABSTRACT FROM AUTHOR]

This article presents a new isolated DC/DC bidirectional converter with soft switching, using a transformer with two voltage taps and two full bridges with insulated-gate bipolar transistors (IGBTs), one on each side of the transformer to be integrated in hybrid systems of renewable energy. A large voltage conversion ratio can be achieved using this converter, in buck and booster modes. Also medium and high DC power can be converted with a good efficiency. Analysis and switching techniques have been reported. To verify the principle of operation, a laboratory prototype of 10 kW has been performed. Experimental results are presented, operating in boost mode. The switching algorithm used has been modelled in MATLAB-Simulink to generate C code. This code has been implemented in a DSP F2812, which has been used to build the prototype. [ABSTRACT FROM AUTHOR]

In recent years, multilevel inverters (MLIs) have emerged to be the most empowered power transformation technology for numerous operations such as renewable energy resources (RERs), flexible AC transmission systems (FACTS), electric motor drives, etc. MLI has gained popularity in medium- to high-power operations because of numerous merits such as minimum harmonic contents, less dissipation of power from power electronic switches, and less electromagnetic interference (EMI) at the receiving end. The MLI possesses many essential advantages in comparison to a conventional two-level inverter, such as voltage profile enhancement, increased efficiency of the overall system, the capability of high-quality output generation with the reduced switching frequency, decreased total harmonic distortions (THD) without reducing the power of the inverter and use of very low ratings of the device. Although classical MLIs find their use in various vital key areas, newer MLI configurations have an expanding concern to the limited count of power electronic devices, gate drivers, and isolated DC sources. In this review article, an attempt has been made to focus on various aspects of MLIs such as different configurations, modulation techniques, the concept of new reduced switch count MLI topologies, applications regarding interface with renewable energy, motor drives, and FACTS controller. Further, deep insights for future prospective towards hassle-free addition of MLI technology towards more enhanced application for various fields of the power system have also been discussed. This article is believed to be extremely helpful for academics, researchers, and industrialists working in the direction of MLI technology. [ABSTRACT FROM AUTHOR]

Biomass is a leading nonhydro resource of renewable energy. The gasifier heats the wood, the final result is a very clean burning gas fuel suitable for direct use in modern power systems such as gas turbines or diesel engines. In this paper a load is fed from these emergency electric units. [ABSTRACT FROM AUTHOR]

Renewable energy integration has been recently promoted by many countries as a cleaner alternative to fossil fuels. In many research works, the optimal allocation of distributed generations (DGs) has been modeled mathematically as a DG injecting power without considering its intermittent nature. In this work, a novel probabilistic bilevel multi-objective nonlinear programming optimization problem is formulated to maximize the penetration of renewable distributed generations via distribution network reconfiguration while ensuring the thermal line and voltage limits. Moreover, solar, wind, and load uncertainties are considered in this paper to provide a more realistic mathematical programming model for the optimization problem under study. Case studies are conducted on the 16-, 59-, 69-, 83-, 415-, and 880-node distribution networks, where the 59- and 83-node distribution networks are real distribution networks in Cairo and Taiwan, respectively. The obtained results validate the effectiveness of the proposed optimization approach in maximizing the hosting capacity of DGs and power loss reduction by greater than 17% and 74%, respectively, for the studied distribution networks. [ABSTRACT FROM AUTHOR]

The optimal reactive power dispatch (ORPD) problem is an important issue to assign the most efficient and secure operating point of the electrical system. The ORPD became a strenuous task, especially with the high penetration of renewable energy resources due to the intermittent and stochastic nature of wind speed and solar irradiance. In this paper, the ORPD is solved using a new natural inspired algorithm called the marine predators' algorithm (MPA) considering the uncertainties of the load demand and the output powers of wind and solar generation systems. The scenario-based method is applied to handle the uncertainties of the system by generating deterministic scenarios from the probability density functions of the system parameters. The proposed algorithm is applied to solve the ORPD of the IEEE-30 bus system to minimize the power loss and the system voltage devotions. The result verifies that the proposed method is an efficient method for solving the ORPD compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

• Development of probabilistic and deterministic optimization problem to tune power system stabilizers. • Use of the new directional bat algorithm. • Detailed comparison of probabilistic and deterministic tuning method. • Testing of controller performance under different critical scenarios. The modern power system consists of a large mixture of renewable energy sources (RES), varying and flexible loads, and is also experiencing a situation where a significant number of conventional generators are being replaced by power electronics based sources. All of these factors may lead to a decrease in the small-signal stability (SSS) margin of the system which in turn can cause power system instability. Power system stabilizers (PSSs) are widely used to improve the low-frequency oscillatory stability of power systems. Currently, there are two popular methods to coordinate multiple PSSs to improve SSS: deterministic and probabilistic method. This paper first formulates the problem of coordinating multiple PSSs to improve SSS as a deterministic and a probabilistic optimization problem which is then solved using the new directional bat algorithm. A detailed comparison between the two design methods under different scenarios based on obtained results is carried out afterward. All of the analysis was carried out on a modified IEEE 68 bus system. The obtained results provide key insights as well as the advantages and limitations of the probabilistic and deterministic approach to optimize the PSS parameters. [ABSTRACT FROM AUTHOR]

In the last few decades, interest in the integration of Distributed Generators (DGs) into distribution networks has been increased due to their benefits such as enhance power system reliability, reduce the power losses and improve the voltage profile. These benefits can be increased by determining the optimal DGs allocation (location and size) into distribution networks. This paper proposes an efficient optimization technique to optimally allocate the multiple DG units in distribution networks. This technique is based on Sine Cosine Algorithm (SCA) and chaos map theory. As any random search-based optimization algorithm, SCA faces some issues such as low convergence rate and trapping in local solutions during the exploration and exploitation phases. This issue can be addressed by developing Chaotic SCA (CSCA). CSCA is mainly based on the iterative chaotic map which used to update the random parameters of SCA instead of using the random probability distribution. The iterative chaotic map is applied for single and multi-objective SCA. The proposed technique is validated using two stranded IEEE radial distribution feeders; 33 and 69-nodes. Comprehensive comparison among the proposed technique, the original SCA, and other competitive optimization techniques are carried out to prove the effectiveness of CSCA. Finally, a complete study is performed to address the impact of the intermittent nature of renewable energy resource on the distribution system. Hence, typical loads and generation (represented in PV power) profiles are applied. The result proves that the CSCA is more efficient to solve the optimal multiple DGs allocation with minimum power loss and high convergence rate. • Integration of distributed generators into distribution networks. • Efficient optimization for allocation of multiple distributed generators. • Sine cosine algorithm and chaos map theory. • Stranded IEEE radial distribution feeders. • The result shows that the proposed optimization technique is efficient. [ABSTRACT FROM AUTHOR]

The growing reliance on hydroelectric power and the risk of future droughts pose significant challenges for power systems, especially in developing countries. To address these challenges, comprehensive long-term energy planning is essential. This paper proposes an optimized electrical system for 2050, using Ecuador as a case study. For forecasting electricity demand, a Neuro-Fuzzy Adaptive Inference System is employed, utilizing real historical data. Subsequently, the EnergyPlan software constructs a long-term energy consumption model, exploring three scenarios based on Ecuador's energy potential. The first scenario represents a 'business as usual' approach, mirroring the current trend in the Ecuadorian electricity system. In contrast to the second scenario, it encompasses a broader range of renewable sources, including offshore wind, pumped storage, biomass, and geothermal energy. The third scenario extends the second one by incorporating demand response systems, such as vehicle-to-grid and hydrogen-to-grid technologies. In terms of novelty, this study highlights the innovative use of the Neuro-Fuzzy Adaptive Inference System for demand forecasting, along with a comprehensive exploration of multiple scenarios to optimize the electrical system. Research findings indicate that the integration of these new renewable energy sources not only reduces electricity import costs but also ensures surplus electricity production. Consequently, it is anticipated that the 2050 electricity system will reduce its dependence on hydroelectric energy while adopting photovoltaic and wind energy with penetration rates of 65 %, 11.2 %, and 9 %, respectively. This transition will be facilitated by a pumped storage system with a 28 % penetration rate and enhanced connectivity with neighboring countries, enabling the seamless integration of electric and hydrogen vehicles. [Display omitted] • Introducing a long-term electricity demand forecast with adaptive neuro-fuzzy inference. • An energy schedule for Ecuador in 2050 is derived from the demand forecast. • Renewable sources reduce costs, surplus electricity, and imports, enabling a 100 % renewable system. • By 2050, Ecuador's electrical system will rely on hydro, photovoltaic, wind, and pumped hydro power. • The optimized Ecuadorian electrical system will enable V2G and H2G integration in 2050. [ABSTRACT FROM AUTHOR]

This article presents a comparison of two alternative systems to supply the traction power of a tramway in Cuenca–Ecuador. Each system studies the effective combination of supercapacitors, lithium ion batteries and proton exchange membrane fuel cells (SC/LIB/PEMFC) on board. The first system uses renewable sources (PV/HKT/GB/Grid) supplying the on-board systems through the existing grid and hydrogen charging stations. While the second system uses only grid power from a single point of charge, leaving the tramway without any external connection point throughout their journey. The energy and economic analyses are based on the capacity of each system to supply the load and the resources used. The results show that the new proposed control systems, by means of the analysed configurations made up of different control states, are always capable of perfectly supplying the power required by the tramway throughout their journey. However, when using energy from renewable sources, hydrogen consumption decreases by 4.27% with respect to the grid on each round trip, with a lower net present cost. Furthermore, in the first proposed system, the depth of discharge in SC and LIB is greater. • Energy control systems based on fuel cell for a hybrid tramway in Ecuador. • Renewable components (photovoltaic, hydrokinetic and biomass). • All components are charged with the braking power of the tramway. • The study includes technical-economic analysis of two proposed systems. [ABSTRACT FROM AUTHOR]

The energy sector of Morocco relies mainly on imported fossil fuels. The expensive import bills associated with fossils, as well as the global drive for greenhouse gas (GHG) emission reduction, have compelled the country to consider the utilization of renewable energy resources such as hydro, wind, and solar for energy generation. Power generation from wind and solar is highly intermittent hence require storage systems to guarantee supply security. Considering the abundant nature of sheep rearing in Morocco, power generation from sheep dung through anaerobic digestion (AD) can serve as an alternative power generation source to alleviate the intermittent situations of wind and solar since biomass supply for AD is not affected by the weather. Therefore, this paper evaluates the capability of hybrid power generation, using AD of sheep dung, wind, and solar in two selected areas of the Fez-Meknes region in Morocco. A mixed-integer linear programming model was implemented in A Mathematical Programming Language (AMPL) using a linear solver of CPLEX. The results of the analysis indicated that only standalone wind generation and hybrid wind/PV/biogas generation are able to meet the total load demand of the two study locations. The hybrid generation resulted in a total carbon emission of 349.30 and 3871.60 g/year at a cost of 13,088.38 and 145,394.13 $/year for Tazouta and Fez respectively in supplying the whole load of the study locations, whiles the standalone wind generator resulted in a total carbon emission of 274 and 2603 g/year at a total cost of 23,352 and 239,500 $/year for satisfying the electric load of these two regions respectively. The results shows that, hybrid solar/wind/PV is the most ideal for supplying power to the two study locations in terms of cost and load satisfaction, while the wind is the most ideal in terms of load satisfaction and carbon emissions. • Sheep dung potential for generating electric power in rural areas of Fez-Meknes. • Wind, solar and biogas electricity generation in water-stressed areas of Morocco. • Hybrid renewable energy generation. [ABSTRACT FROM AUTHOR]

This research presents a 100% renewable system configured based on its real potential and use of renewable energies for the Ecuadorian Amazon, considered one of the places with the greatest diversity of native plant species on the planet. The transition processes that are born from the central state seek to take a radical turn in the electricity sector but there is still no defined roadmap, this is where this research will allow it to be an important reference for its long-term transformation. The current system is based on fossil fuels and in this study an energy transition process is planned with the support of the EnergyPLAN tool that puts into practice the concept of intelligent energy, progressively and systematically achieving 100% renewable energy for 2050. Finally, after analyzing the results, it is concluded that the energy mix for 2050 can consider 36.47% hydro, 33.04% solar photovoltaic, 29.73% wind energy and other technologies to a lesser extent with 0.74%. Surpluses can be injected into the interconnection sites with Colombia and Peru, also achieving economic income to preserve and expand the infrastructure of the electricity sector. Decision makers can consider this study as a reference before committing economic resources and carrying out modern energy planning. [Display omitted] • A smart Renewable Energy approach is presented. • A convergent method is developed for greater contributions from wind and solar PV. • The proposal launched is to leave the oil underground. [ABSTRACT FROM AUTHOR]

In order to mitigate the influence of global warming and greenhouse gasses emission, different proceedings are suggested such as utilizing renewable energies and demand response programs. This paper investigates the expansion of renewable resources and electrical energy storage units in distribution systems towards reducing investment costs and environmental pollution. Since the development of components is not possible in a single-stage due to the limitation of staff and funds, a multi-stage programming is applied to consider various restrictions. According to the penetration of crypto-currency miners in recent years, their impact is evaluated on the problem as the high-rate energy consumers. Moreover, the demand side management strategy and risk-averse scenario-based approach are implemented to analyze the role of responsive loads and model the renewable resources uncertainty, respectively. The results approve that simultaneous expansion of wind turbines, photovoltaics and electrical storage systems decreases the total pollution and cost by 100% and 99.98% after the third year, respectively. The simulations also validate that crypto-currency miners reduce the total revenue by 4.16%, whereas the responsive loads increase it by 9.89%. As well, the fluctuations of wind and solar power decrease the total revenue by 13.27%, in return, the robustness notably improves. [ABSTRACT FROM AUTHOR]

When it comes to biomass production from agricultural residues, Africa is playing a vital role due to the reliance of many countries within the continent on agriculture as the main Gross Domestic Product (GDP) contributor. Africa has the opportunity to leverage residues from its high agricultural production to bridge the electricity access deficit. Therefore, the objective of this study is to analyze the trend in crop production, and the contribution of crop residues for clean power generation in Africa from 1990 to 2021, using data from existing literature, as well as Food and Agriculture Organization Statistics (FAOSTAT)| and International Energy Agency (IEA) online database. The analysis showed that yam, plantain, cassava, sorghum, and millet production are concentrated in Africa, accounting for 97 %, 64 %, 63 %, 49 %, and 48 % of global production, respectively. Analyzing the trend in bioelectricity generation indicates that, biopower production in Africa only increased significantly between 1990 and 2006 followed by a steep decrease from 2006 to 2007 due to policy and technical failure. It was found that the highest contribution of bioelectricity in electric energy consumption is related to the year 2006 (0.41 %). This peak contribution dropped to 0.28 % in 2020. With the increasing trend in crop production in Africa and the decrease in bioelectricity generation and consumption, it is concluded that crop residue production alone cannot be a good measure of biomass potential for bioelectricity generation as reported in many studies without looking at other driving factors. Therefore, the study has proposed factors for increasing bioelectricity generation in Africa. [ABSTRACT FROM AUTHOR]