Search

Generation planning in the power system has been a complex and challenging multi-objective optimization problem. Numerous methodologies have been developed and applied to solve this problem. Still, researchers look forward to further improved methods that can further enhance the outcomes for this problem. This article suggests a hybrid FPA-PPSO scheme that combines the flower pollination algorithm (FPA) and phasor particle swarm optimization (PPSO) algorithm to solve this problem. The suggested FPA-PPSO scheme includes a high search efficiency, balanced local and global search capabilities, etc. which can help in more efficient planning of the generation in a microgrid. To analyze the supremacy of the suggested FPA-PPSO scheme over FPA and PPSO methods, a comparative study using twenty-three benchmark functions is presented. From the analysis conducted from the Friedman test, the suggested method gets a rank of 1.59 which is better compared to PPSO and FPA which get ranks of 2.61 and 3.04 for the twenty-three benchmark functions. Further, the suggested method is tested using four test cases to show its effectiveness in solving the scheduling problem of a microgrid considering renewable energy sources, energy storage systems, and demand response management. The obtained results conclude that the suggested methods show promising performance in planning the generating unit. Using the DRM scheme further helps to efficiently schedule the generation as the utilities save up to $ 25545 more when DRM planning is considered. [ABSTRACT FROM AUTHOR]

The article contains a description of the design solutions proposed by the authors for a hybrid wind turbine bearing, in which the sliding part takes over the load to the turbine shaft after reaching the shaft rotation speed, ensuring hydrodynamic lubrication of the plain bearing and relieving the rolling bearing. This allows for low starting resistance of the power plant and ensures quiet operation during use. Two conceptual solutions of a hybrid bearing were presented, differing in the shape of the plain bearing journal. A mechanism for automatic switching of the load between a rolling and a plain bearing was developed. A solid simulation model of this mechanism was built in the Autodesk Inventor—Dynamic Simulation software Inventor Professional 2023 environment, and its operation was simulated. The results confirmed the usefulness of using this design in shaft-bearing systems of wind turbines with a vertical axis of rotation. Based on the simulation, the speed at which the thrust roller bearing will be released was determined. Technical parameters of a plain bearing with a spherical journal shape were calculated. The height of the lubrication gap and the shaft rotational speed at which the bearing load capacity index reaches a critical value were determined. [ABSTRACT FROM AUTHOR]

With a single-switched DC-DC converter, a hybrid approach is proposed for a fast-charging (FC) dc to dc converter with the controller for an electric vehicle charging station (EVCS) that combines solar photovoltaic (PV), fuel cells (FC), and a battery energy storage system (BESS). The proposed hybrid technique integrates a radial-basis-function-neural-network (RBFNN) and a student-psychology-optimization-algorithm (SPOA); commonly known as the RBFNN-SPOA technique. The PV-FC hybrid electric vehicle systems, like controllers, electric vehicle, and the vehicle's power source, are displayed. The proposed converter architectures have less voltage waveform changes, a low voltage stress on semiconductor power sources, and a high-voltage ratio of conversion. The proposed method is to use FC successfully at battery charge states and varied radiation levels. The FC in the controller of SPOA may change the inputs and outputs depending on the battery state of charge (SoC) and PV irradiation. Similarly, the needed input-torque for the motor is forecasted by the recalling recurrent neural network controller using several manually produced torque signals. The performance of the proposed technique is evaluated in MATLAB and is compared to ant colony optimization (ACO), crow optimization algorithm (COA), and fuzzy logic controller (FLC) approaches. According to the results, the efficiency of the proposed technique is 99.17%. [ABSTRACT FROM AUTHOR]

Abstract With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices.

Abstract In the operation and control of power systems, load frequency control (LFC) plays a critical role in ensuring the stability and reliability of interconnected power systems. Modern power systems with significant penetration of highly variable and intermittent renewable sources present new challenges that make traditional control strategies ineffective. To address these new challenges, this paper proposes a novel LFC strategy that employs a cascaded fractional-order proportional integral-fractional-order proportional integral derivative with a derivative filter (FOPI-FOPIDN) as a controller. The parameters of the FOPI-FOPIDN are optimised using a variant of the particle swarm optimization (PSO) in the literature called ADIWACO. The effectiveness and scalability of the proposed strategy are validated by extensive simulations conducted on two- and three-area test systems and performance comparisons with recent LFC control strategies in the literature. The performance metrics used for the evaluation are ITAE values, deviations in the power flows in the tie-lines, and deviations in the frequencies of the control areas with the power systems subjected to diverse load and RES generation disturbances in several experimental scenarios. Governor dead band, communication time delay, and generation rate constraints are considered in one of the scenarios for more realistic evaluation. Again, the controller’s robustness to uncertain model parameters is validated by varying the parameters of the three-area test system by ± 50%. The simulation results obtained confirm the controller’s robustness and its superiority over the comparison LFC strategies in terms of the above performance metrics.

The presented work addresses the growing need for efficient and reliable DC microgrids integrating renewable energy sources. However, for the sake of practicality, implementing complex control strategies can increase system complexity. Thus, efficient methodologies are required to provide efficient energy management of microgrids while increasing the integration of renewable energy sources. The primary contribution of this work is to investigate the issues related to operating a DC microgrid with conventional control designed to power DC motors using readily available, non-advanced control strategies with the objective of achieving stable and reliable grid performance without resorting to complex control schemes. The proposed microgrid integrates a combination of uncontrollable renewable distributed generators (DGs) alongside controllable DGs and energy storage systems, including batteries and supercapacitors, connected via DC links. The Incremental Conductance (InCond) algorithm is employed for maximum power point tracking to maximize power output from the PV system. The energy management strategy prioritizes the solar system as the primary source, with the battery and supercapacitor acting as backup power sources to ensure overall system reliability and sustainability. The effectiveness of the microgrid under various operating conditions is evaluated through extensive simulations conducted using MATLAB. These simulations explore different power generation scenarios, including normal operation with varying load levels and operation under Standard Test Conditions (STC). Moreover, fault analysis of the DC microgrid is performed to examine system reliability. The system performance is evaluated using real-time simulation software (OPAL-RT) to validate the effectiveness of the approach under real-time conditions. This comprehensive approach demonstrates the efficacy of operating a DC microgrid with conventional controllers, ensuring grid stability and reliability across various operating conditions and fault scenarios while prioritizing the use of renewable energy sources. The results illustrated that system efficiency increases with load, but fault tolerance measures, can introduce trade-offs between reliability and peak efficiency. [ABSTRACT FROM AUTHOR]

With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices. [ABSTRACT FROM AUTHOR]

Among the most significant non-linear challenges for power network design and smooth functioning of current modern updated power system networks is the optimum power flow (OPF) problem. Importance of the electrical system modeling has recently come to light due to the incremental use of energy from renewable sources in power systems networks. The goal is to use wind, solar and tidal sources to recreate the issue of OPF. In this work, Weibull, Lognormal, and also Gumbel probability distribution functions were applied to simulate the uncertainties of wind, photovoltaic, and tidal energy system. Additionally, by adding test scenarios of unpredictable wind, solar and tidal energy systems involving minimization of the cost function, loss of active power, voltage deviation, and increase stability of voltage. In accordance with the chosen thermal producing units, the solutions were evaluated using different locations using IEEE 30-bus systems of testing that incorporate renewable energy sources. The proposed power system planning problem was solved by multi-objective function where unified power flow controller are utilized as flexible AC transmission systems controllers via recently introduced optimization algorithms and the simulation outcomes of the aforementioned technique have been compared with Multi Objective Adaptive Guided Differential Evolution algorithms. The adaptive improved flower pollination algorithm (AIFPA) is a strong and reliable algorithm that is presented in this work. The AIFPA can efficiently deal with many kinds of high-complexity objective regions in multi-objective optimization situations. Utilizing an IEEE 30-bus test system, the suggested approaches' performance was examined and evaluated for a range of objective functions. The simulation results obtained from the proposed algorithm were effective in finding the optimal solution compared to the results of the meta-heuristic algorithm and were reported in the literature. [ABSTRACT FROM AUTHOR]

As the share of renewable energy sources in the power system keeps growing, a new issue with frequency stability – diminishing and changing inertia – has emerged. Estimating power system inertia has received a lot of attention lately in order to market design of synthetic inertia as an additional service and implement timely corrective steps for robust frequency control. Curve fitting technique has emerged as one of the promising techniques for estimation of power system inertia, however, many challenging aspects need to be addressed for its practical applications. This study aims to systematically assess the effectiveness of the curve fitting method under a range of operational parameters and conditions, such as the curve fitting order, the length of data to be used for curve fitting after an event begins, and the length of the moving average filter applied to the frequency data to remove oscillatory and noise from the frequency transient. The simulation studies have been performed on DIgSILENT and MATLAB platforms. The results show that very high and very low order curve fitting results are high errors and the 5th order model with mean error in estimated value of inertia less than 1% has outperformed others. The analysis on various window width of moving average filter found that increasing the window width results in more error. For IEEE-9 bus system, a filter up to 850 ms width has a mean percentage error below 1%. The analysis also revealed that the duration of the data used for curve fitting is not significant. For variation of the duration of the data used for the curve fitting from 50 ms to 500 ms, the estimated inertia values are found to be in a close range of 2.4881 s to 2.5262 s, for the single machine system with H = 2.5 s. The analysis presented in this study will be useful for effective application of curve fitting method in inertia estimation. [ABSTRACT FROM AUTHOR]

This article presents a novel approach to enhance the efficiency, control, and power management capabilities of traditional Zeta Converters in renewable energy applications. The proposed particle swarm optimization (PSO)-modified three-port zeta converter (MTPZC) features a multiport structure with dedicated input ports for photovoltaic (PV) and battery sources, along with an output port. The integration of PSO optimization aims to optimize maximum power point tracking to enhance energy extraction. Extensive simulations and experimental validations showcase the MTPZC's superior performance in power extraction from both PV and Battery sources. This innovative design positions the converter as a promising solution for multiport energy conversion systems in renewable energy applications. The article contributes to the advancement of power electronics by offering improved reliability and efficiency. Simulation results, validated in MATLAB and compared with existing methods, demonstrate an impressive efficiency of 97.8%. The MTPZC outperforms existing approaches, indicating lower converter losses and highlighting its potential for practical implementation in diverse renewable energy systems. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

In the operation and control of power systems, load frequency control (LFC) plays a critical role in ensuring the stability and reliability of interconnected power systems. Modern power systems with significant penetration of highly variable and intermittent renewable sources present new challenges that make traditional control strategies ineffective. To address these new challenges, this paper proposes a novel LFC strategy that employs a cascaded fractional-order proportional integral-fractional-order proportional integral derivative with a derivative filter (FOPI-FOPIDN) as a controller. The parameters of the FOPI-FOPIDN are optimised using a variant of the particle swarm optimization (PSO) in the literature called ADIWACO. The effectiveness and scalability of the proposed strategy are validated by extensive simulations conducted on two- and three-area test systems and performance comparisons with recent LFC control strategies in the literature. The performance metrics used for the evaluation are ITAE values, deviations in the power flows in the tie-lines, and deviations in the frequencies of the control areas with the power systems subjected to diverse load and RES generation disturbances in several experimental scenarios. Governor dead band, communication time delay, and generation rate constraints are considered in one of the scenarios for more realistic evaluation. Again, the controller's robustness to uncertain model parameters is validated by varying the parameters of the three-area test system by ± 50%. The simulation results obtained confirm the controller's robustness and its superiority over the comparison LFC strategies in terms of the above performance metrics. [ABSTRACT FROM AUTHOR]

This review provides a comprehensive exploration of distributed generation (DG) in the context of radial distribution systems, encompassing various DG technologies, current advancements, and their integration challenges. It evaluates the impacts of DG on distribution networks, highlighting diverse applications, drivers, and benefits. A significant focus is on the optimal integration of DG, assessing various optimization techniques, algorithms, and objective functions used to address this challenge. Through a detailed analysis of existing literature, this study identifies that no single optimization method stands out as universally superior. Instead, the research concludes that hybrid optimization techniques, particularly those combining heuristic methods such as genetic algorithms with other approaches, offer the most effective and reliable solutions for the complex task of DG integration. These hybrid methods leverage the strengths and mitigate the weaknesses of individual techniques, providing more robust and dependable outcomes. This conclusion underscores the need for innovative, multi-faceted approaches to optimize DG integration in evolving distribution systems. [ABSTRACT FROM AUTHOR]

Globally, grid systems are facing substantial challenges due to the rapid growth in power demand. New technologies equipped by means of smart energy resources are one promising solution to cope with this challenge, leading to microgrid systems. The growing demand to develop the power sector by utilizing alternative energy resources plays an influential role in the advancement of low-voltage DC (LVDC) systems. With the prompt development of LVDC distribution grids, bipolar LVDC distribution topology is becoming a leading and promising solution. Therefore, this review paper represents a comprehensive overview as well as a comparative and critical analysis for the development of novel methods on LVDC grid analysis, control strategy, and protection schemes to solve energy management problems that lead to a futuristic novel solution in the green revolution. [ABSTRACT FROM AUTHOR]

ABSTRACTThis study demonstrates the optimal design of a hybrid renewable energy system for the electrification of a potential rural national park reserve. The objective is to evaluate the feasibility of utilising renewable energy sources (RESs) to reduce GHG emissions. The core components studied are photovoltaic solar (PV) panels, wind turbines (WTs), diesel generators (DGs), and battery banks (BBs). The research involves estimating the reserve’s load profile, assessing the potentials of RESs, and designing various system configurations. Each configuration is evaluated to derive the most efficient option. For a building with an energy demand of 832,640 kWh/yr, it is found that PV-WT-BB is the best configuration which comprises of 500-kW PV, 200-kW WT, and 1424-kW BBs and contributes an annual generation of 1,821,732 kWh/year. With a net present cost of $2,206,308 for the lifetime of the project, it saves 757,162 kg/year of total GHG emissions if the reserve operated using DGs.

The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development. In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area.

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model's robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel "policy-versus-policy" strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R 2 , MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF. [ABSTRACT FROM AUTHOR]

The strategic objective of world climate policy is the decarbonization of industries, aiming to achieve "net-zero" emissions by 2050, as outlined in the European Green Deal and the Paris Agreement. This transition entails increasing the utilization of renewable energy sources (RES) in industrial energy consumption, thereby transforming economies from reliance on fossil fuels to sustainable alternatives. However, this shift poses a significant challenge for many EU countries, with varying degrees of success in adaptation. This paper investigates the process of decarbonizing industries by analyzing trends in the adoption of RES in EU countries and evaluating their progress toward climate targets. Utilizing time series analysis of production, total energy usage, and the proportion of renewables in industrial energy consumption, the study compares two groups of countries: longstanding EU members and newer additions. The aim is to forecast the trajectory of RES integration in industry and assess the feasibility of meeting the targets outlined in the European Green Deal. The findings reveal a considerable gap between the set targets and projected outcomes, with only a few countries expected to meet the EU's 2030 goals. This is highlighted by disparities in RES shares across member states, ranging from 0.0% to 53.8% in 2022. Despite notable increases in the absolute use of renewable energy, particularly in central and eastern European nations, substantial challenges persist in aligning industrial sectors with EU decarbonization objectives. [ABSTRACT FROM AUTHOR]

The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development. In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area. [ABSTRACT FROM AUTHOR]

The presented work addresses the growing need for efficient and reliable DC microgrids integrating renewable energy sources. However, for the sake of practicality, implementing complex control strategies can increase system complexity. Thus, efficient methodologies are required to provide efficient energy management of microgrids while increasing the integration of renewable energy sources. The primary contribution of this work is to investigate the issues related to operating a DC microgrid with conventional control designed to power DC motors using readily available, non-advanced control strategies with the objective of achieving stable and reliable grid performance without resorting to complex control schemes. The proposed microgrid integrates a combination of uncontrollable renewable distributed generators (DGs) alongside controllable DGs and energy storage systems, including batteries and supercapacitors, connected via DC links. The Incremental Conductance (InCond) algorithm is employed for maximum power point tracking to maximize power output from the PV system. The energy management strategy prioritizes the solar system as the primary source, with the battery and supercapacitor acting as backup power sources to ensure overall system reliability and sustainability. The effectiveness of the microgrid under various operating conditions is evaluated through extensive simulations conducted using MATLAB. These simulations explore different power generation scenarios, including normal operation with varying load levels and operation under Standard Test Conditions (STC). Moreover, fault analysis of the DC microgrid is performed to examine system reliability. The system performance is evaluated using real-time simulation software (OPAL-RT) to validate the effectiveness of the approach under real-time conditions. This comprehensive approach demonstrates the efficacy of operating a DC microgrid with conventional controllers, ensuring grid stability and reliability across various operating conditions and fault scenarios while prioritizing the use of renewable energy sources. The results illustrated that system efficiency increases with load, but fault tolerance measures, can introduce trade-offs between reliability and peak efficiency.

The rising demand for electrical energy, coupled with the running down of conventional energy sources, has prompted vast research into renewable energy sources (RES) for power generation. Wind and solar photovoltaic energies are among the most critical resources, despite their susceptibility to fluctuating climatic conditions. Power electronics have been helpful in the integration of renewable energy sources (RESs) with grid, and multilevel inverters (MLIs) have arisen as critical components for the effective utilization of energy from RES and the enhancement of power quality. This paper provides an extensive overview of the latest developments in MLI topologies and how they have been applied in different industries. Optimal MLI designs for various applications will be identified by a thorough investigation of performance parameters including component count, total harmonic distortion (THD), and overall efficiency. Additionally, the paper explores the role of MLIs in diverse sectors including RES, microgrids, and residential applications. By synthesizing insights from recent literature and performance evaluations, this review aims to provide valuable guidance for researchers and practitioners in selecting the most suitable MLI topology for specific applications.

To reduce the carbon footprint of buildings, the concept of virtual prosumers (consumers who both consume and produce) using remote solar energy parks represents a novel method in Europe. In 2019, Lithuania became the first country in Europe to introduce a digital platform that enables the buying or renting of parts of a remote solar park, making it the first such platform in the world to operate on a national scale. This study examines the effectiveness of this model in Lithuania, assessing the model’s success, public engagement, and success factors. The main study focus is on evaluating the impact of remote solar parks on the decarbonization of buildings, particularly through the prism of virtual prosumer participation. This study integrates both qualitative and quantitative data. The quantitative analysis includes a detailed case study, evaluating the amount of energy produced by two selected remote solar parks in Lithuania, as well as their impact on the carbon dioxide emissions and primary energy use of the two individual houses (a detached house and a unit within an apartment building) connected to these remote power plants. In Case Study A, the renewable primary energy usage was 22.19 kWh/m2 compared to a minimal 0.22 kWh/m2 of non-renewable energy (CO2 emissions 0.0 kgCO2/kWh). Case Study B showed 181.38 kWh/m2 of renewable energy versus 3.63 kWh/m2 of non-renewable energy (CO2 emissions 6.17 kgCO2/kWh). Concurrently, qualitative methods involve analysing the existing legal and economic frameworks in Lithuania and Europe, which either facilitate or impede the prosumer model, in addition to examining the necessary technological infrastructure. Key findings of this study highlight the potential of remote solar energy parks to significantly reduce the carbon emissions of buildings. This model is especially beneficial for structures where onsite solar energy solutions are impractical. It fosters greater inclusivity in adopting renewable energy, enabling a variety of stakeholders to participate in and benefit from clean energy production. However, the study identifies several major challenges, including regulatory restrictions, the need for infrastructure development, a shortage of developers, state contributions, public awareness, and the creation of a unified platform.

The advanced control technique Model predictive control (MPC) is gaining popularity in power electronics for converters with distributed energy resources. It combines closed-loop control and minimizes errors and control effort. As a model-based control technique, MPC's performance can degrade due to plant disturbances, such as parametric errors or large perturbations in grid voltage or load current. Our research used an MPC with modulation on a converter connected to the grid with an inductive filter for integrating renewable energy sources. The margin of robust stability (RS), derived from the singular Value Decomposition (SVD), provides a theoretical investigation of the robustness of the MPC controller tuning in dependency on the cost function weight factors and the time horizons. In the experiments conducted on a 2 kW workbench, it was confirmed that the proposed controller is stable and robust in nominal and under severe parametric uncertainty conditions, addressing the power quality criteria defined in IEEE Std. 519-2014. The experimental results show that the proposed MPC controller tuning outperforms the classical PI current controller in nominal conditions and is more robust to uncertainty in the passive filter of the grid-connected converter.

The article contains a description of the design solutions proposed by the authors for a hybrid wind turbine bearing, in which the sliding part takes over the load to the turbine shaft after reaching the shaft rotation speed, ensuring hydrodynamic lubrication of the plain bearing and relieving the rolling bearing. This allows for low starting resistance of the power plant and ensures quiet operation during use. Two conceptual solutions of a hybrid bearing were presented, differing in the shape of the plain bearing journal. A mechanism for automatic switching of the load between a rolling and a plain bearing was developed. A solid simulation model of this mechanism was built in the Autodesk Inventor—Dynamic Simulation software Inventor Professional 2023 environment, and its operation was simulated. The results confirmed the usefulness of using this design in shaft-bearing systems of wind turbines with a vertical axis of rotation. Based on the simulation, the speed at which the thrust roller bearing will be released was determined. Technical parameters of a plain bearing with a spherical journal shape were calculated. The height of the lubrication gap and the shaft rotational speed at which the bearing load capacity index reaches a critical value were determined.

The characterization of electric vehicles as environmentally friendly means of transportation hinges, on the one hand, upon the manner in which the energy for their charging is generated and, on the other hand, on the recyclability of the materials composing them, with primary emphasis on the recycling of batteries. Given that we are still in the early stages of electrification in road transportation, it can be argued that at least a decade is required for the development of a sustainable battery recycling industry. Conversely, the progressively increasing number of electric vehicles makes the necessity of charging them with clean, green energy imperative. In this context, this study examines the energy and economic aspects of replacing 50% of the public passenger vehicle fleet, which currently relies on internal combustion engines, with electric vehicles on the island of Zakynthos, Greece. Specifically, it calculates the energy needs of these vehicles and proposes methods for environmentally friendly electricity generation to meet the electrical demand. To assess the benefits for the owners of the charging stations and the electric vehicles, the Life-Cycle Cost Analysis (LCCA) method is employed for various scenarios regarding (a) the pricing of the supplied electrical energy for electric vehicle charging and (b) the evolution of fossil fuel prices. The study concludes by highlighting the environmental advantages of such an investment. [ABSTRACT FROM AUTHOR]

The decrease in overall inertia in power systems due to the shift from synchronous generator production to renewable energy sources (RESs) presents a significant challenge. This transition affects the system's stable frequency response, making it highly sensitive to imbalances between production and consumption, particularly during large disturbances. To address this issue, this paper introduces a novel approach using Multivariate Empirical Mode Decomposition (MEMD) for the accurate estimation of power system inertia. This approach involves applying MEMD, a complex signal processing technique, to power system frequency signals. The study utilizes PMU (Phasor Measurement Unit) data and simulated disturbances in the IEEE 39 bus test system to conduct this analysis. MEMD offers substantial advantages in analyzing multivariate data and frequency signals during disturbances, providing accurate estimations of system inertia. This approach enhances the understanding of power system dynamics in the context of renewable energy integration. However, the complexity of this methodology and the requirement for precise data collection are challenges that need to be addressed. The results from this approach show high accuracy in estimating the rate of change of frequency (RoCoF) and system inertia, with minimal deviation from actual values. The findings highlight the significant impact of renewable energy integration on system inertia and emphasize the necessity of accurate inertia estimation in modern power systems. [ABSTRACT FROM AUTHOR]

This research article presents a comprehensive investigation into the design, optimization, and performance analysis of a hybrid stand-alone microgrid for an industrial facility in Iraq at coordinates 36.51 and 43.99. The system consists of photovoltaic (PV) modules, inverters, a battery energy storage system (BESS), a generator, and AC loads. Leveraging the capabilities of PVsyst version 7.3.1, HOMER Pro version 3.14.2 and SAM version 2022.11.21 software tools, this study assesses the feasibility and functionality of the hybrid stand-alone microgrid. In this study, PVsyst software is used for detailed designing and analysis of a PV plant, and the PVsyst design file is then used in HOMER Pro software to optimize and design the proposed hybrid stand-alone microgrid, and for detailed performance analysis SAM software is employed. This paper also investigates the impact of ground clearance and ground albedo on the annual generation of bifacial PV modules at various tilt angles. Key findings include a promising normalized production rate of 4.53 kWh/kWp/day with a performance ratio of 0.815 and annual energy production estimates of 84.31 MWh (P50), 79.57 MWh (P90), and 78.24 MWh (P95) for monofacial PV modules, highlighting the system's potential for renewable energy generation. Notably, this research demonstrates the hybrid stand-alone microgrid's capacity to significantly reduce CO2 emissions, saving approximately 1811.6 tons over a 30-year period, thus contributing to sustainability and environmental conservation goals. Additionally, this study reveals operational challenges during the winter months, necessitating generator support to meet load demands. The successful installation and experimental validation of the hybrid stand-alone microgrid underscore its practical viability and its role in advancing clean energy solutions. This research provides valuable insights into hybrid stand-alone microgrid design, emphasizing its importance in ensuring reliable power supply and environmental stewardship. [ABSTRACT FROM AUTHOR]

There is a rapid increase in power demand which has led to an increase in the need for smaller grids all over the world. The microgrid is such a small power supply system that consists of loads, distributed energy resources, and energy storage systems. With the development of the internet, the power sector has started the development of the newest type of grids, involving a smart technology called smart grids. Smart grids have got smart integration and advanced storage, along with dynamic optimization of grid operation and resources, digital information and control, distributed resources, demand-side resources, energy efficiency resources, and smart metering systems. The developments in intelligent controllers reflect in power flow management also. This paper gives an idea of how to achieve power flow management through a tariff-based Fuzzy Logic Controller (FLC). The control algorithm used here is the k factor-based Icosϕ algorithm, which decides the amount of power shared by microgrids and the grid based on the load demand. The power forecasting of the microgrid with a solar source is performed using ANN. The regression plot and the error histogram obtained show the accuracy of the ANN controller. The simulation of power flow management of the smart microgrid system is tested and analyzed and the proposed system is compared with other existing research. The hardware implementation of the FLC controller with the Arduino interface which is analogous to real-time measurement is explained in detail to compare the simulation results obtained. [ABSTRACT FROM AUTHOR]

The reasons (use of fossil fuels as a heat carrier) and consequences (prolonged energy and economic crises) of global warming are analyzed. It is shown that developing countries, using more advanced technologies, particularly in electricity production, are gaining global markets for raw materials and fuel. The vector of technological growth is shifting to Asian and South American countries. The global structure of electricity production by fuel types in countries with the highest electricity consumption is examined. According to the International Energy Agency (IEA) data, in 2019, the majority of electricity production came from fossil sources, a small part from nuclear energy (10.3%), and 26.7% from renewable energy sources (RES). It is demonstrated that half of the world's electricity production is concentrated in three countries: China (30%), the United States (15.5%), and India (6%). Significant contributions are made by EU countries, accounting for 11.2% of global production (including 37.1% from RES). Global renewable energy capacity was 16.7% in 2010, 26.3% in 2019, and 28.7% in 2021. This growth is attributed to the UN's climate policy, aiming at decarbonizing the global economy, and substantial investments in the energy sector. Challenges hindering further growth of renewable energy share in China and other countries are discussed, including the insufficient number of energy storage systems (partly due to high entry costs ranging from 200 to 600 dollars per kWh) and the need for long-distance energy transmission. China, a global leader in renewable energy usage, generates almost 50% of the world's renewable energy capacity. Solar energy, including space projects, is considered the most promising among RES. However, a complete transition to RES is currently impossible due to various reasons. The key problem lies in ensuring an uninterrupted energy supply to consumers. Therefore, there is a need to accelerate the development of powerful energy storage systems and power lines for end consumers. [ABSTRACT FROM AUTHOR]

The technological development in the area of power electronics has paved the way for the construction of High Voltage Direct Current (HVDC) systems. The utilization of HVDC grids alongside conventional High Voltage Alternating Current (HVAC) grids poses several challenges, especially, from stability and control points of view. Indeed, moving towards such systems in the context of conventional Alternating Current (AC) power systems cannot be possible without ensuring the overall stability of hybrid HVAC/HVDC grids. This paper analyzes different aspects of the stability of Voltage Source Converter (VSC)-based HVDC grids and presents various methods of improving stability based on a systematic and comprehensive review. In addition, this paper provides a concise classification of various control methods to improve the operation of such grids and the advantages of each method.

It is evident that renewable energy sources (RES), will soon be considered as primary energy source in electrical networks. However, the increased penetration of RES along with the variable charging profile of electric vehicles in the distribution grid will pose serious technical challenges such as network instability, protection malfunctioning, aggravated line, and low- and high-voltage ride through capabilities. Moreover, the frequent occurrence of natural disasters has posed different dimensional problems for distribution system engineers. Hence, owing to the immediate demand for resilient electrical systems, many researchers have presented planning and operating techniques for resiliency enhancement. Consequently, application of low-voltage ride through (LVRT) during grid transformation in small clusters has significant role. However, the implementation of LVRT in operational methods during the transformation of an electric grid after a natural disaster has a lean share in the literature. Moreover, the applicability of grid code during microgrid formation due to extreme events also drives the planning and operational methodology. Therefore, this article first substantiates the importance of grid codes and IEEE standards for the interconnection of RES plants into a grid. Additionally, the paper presents a succinct summary on various control strategies to overcome the challenges of fault ride through capability, reactive power support, and anti-islanding in integrated wind and solar plants. Thenceforth, the importance of LVRT visibility in resiliency enhancement validated by simulating the urban electrical grid using the DIgSILENT PowerFactory Software.

The fact that they are unpredictable and unreliable, renewable energy sources (RES) are increasingly crucial to the electrical power grid's ability to maintain energy continuity. The challenge of energy sustainability can only be solved by energy storage technology. Regarding a Hybrid Energy Storage System (HESS) powered by a 4-wire, 3-phase grid connected solar Photovoltaic (PV) power system, a novel Smart Energy Control Technique (SECT) with a Whale optimization algorithm is presented in this work. The sustainability of energy via solar PV power generating systems is achieved by using HESS, which consists of batteries and ultra-capacitor storage devices for energy. The suggested SECT has been used for the analysis and experimental testing of several HESS operating scenarios. During experimental testing, the load status of a single sunny day and the PV power outline were constructed and dynamically assessed by utilizing SECT; some test findings in eight distinct operating modes are included in this paper. The system uses 1320W of electricity to charge the battery group, and in one of its modes of operation, the leftover energy is sent to the grid using 5% present harmonics. And because of the system's high power density, quick reaction, and great efficiency, the HESS is a highly efficient energy storage method. Simulation findings and laboratory testing have validated the suggested system.

The time resolution of scheduling plan refers to the length of each time interval in the scheduling plan. With the increasing proportion of climate-sensitive renewable energy sources (RES), the volatility of net load during the dispatch interval is significantly enhanced, resulting in an insufficient system climbing capacity and abnormal frequency. Therefore, the setting of time resolution at different renewable energy penetration rates becomes an urgent problem to be solved at present. A global sensitivity-based day-ahead dispatch time resolution selection optimization method is proposed to select the time resolution for different volatility grids. To achieve a balance between the degree of refinement and the accuracy of load prediction, the global sensitivity analysis method based on Sobol' method and sparse chaos expansion is used to evaluate the impact of net load volatility and uncertainty on dispatch at different time resolutions, and an appropriate time resolution is chosen to optimize the scheduling effect. The analysis and simulation results show that the choice of time resolution is mainly determined by the net load fluctuation rate. The appropriate time granularity is chosen according to the net load fluctuation rate, which minimizes the unbalanced power, and the purpose of improving the optimal scheduling effect and reducing the dispatching cost is achieved.

The role of hydrogen as a clean energy source is a promising but also a contentious issue. The global energy production is currently characterized by an unprecedented shift to renewable energy sources (RES) and their technologies. However, the local and environmental benefits of such RES-based technologies show a wide variety of technological maturity, with a common mismatch to local RES stocks and actual utilization levels of RES exploitation. In this literature review, the collected documents taken from the Scopus database using relevant keywords have been organized in homogeneous clusters, and are accompanied by the registration of the relevant studies in the form of one figure and one table. In the second part of this review, selected representations of typical hydrogen energy system (HES) installations in realistic in-field applications have been developed. Finally, the main concerns, challenges and future prospects of HES against a multi-parametric level of contributing determinants have been critically approached and creatively discussed. In addition, key aspects and considerations of the HES-RES convergence are concluded. [ABSTRACT FROM AUTHOR]

Many countries, including Indonesia, have abundant renewable energy sources (RES), but the share of RES in the current national energy supply is still insignificant. The study aimed to investigate and provide the most feasible combinations of RES that meet domestic electricity demand. For Java and Bali, Indonesia, initially, 35 scenarios, given 4 primary RES (solar, wind, hydropower, geothermal) and municipal solid waste, were assessed based on economic and environmental indicators. This explorative data-driven study found that the existing capacity could only meet 51% of the electricity demand. However, the proposed energy mixes could cover 100% of the electricity demand in 2020 with a required capacity of 8.32–19.10 GW, varying on each scenario. The feasible energy mixes can reduce CO2 emissions by 90–94% compared to a fossil energy mix with gas-fired power plants. The installation, and operation and maintenance costs per life cycle can range from 29–50 and 4–16 billion dollars. The wind-based energy mix, with installed capacities of geothermal (1.16 GW), hydropower (2.87 GW), solar (0.003 GW) and municipal solid waste (0.18 GW) in 2020, showed the highest return on investment (139% ROI) and smallest CO2 emission with highest CO2 reduction (94%). This study provides a scientific method of selecting, projecting, and evaluating viable RES combinations for generating electricity without using fossil fuels. [ABSTRACT FROM AUTHOR]

Road transportation accounts for about 20% of the total GHG emissions in the EU. Nowadays, the substitution of conventional fossil fuel-based ICEs with electric engines, or their hybridization, operating along with Energy Storage Systems, seems to be the most appropriate measure to achieve reductions in both fuel consumption and GHGs. However, EVs encounter crucial challenges, such as long charging time and limited driving range. Hence, the transition to the mass adoption of EVs requires considerable effort and time. However, significant steps have been taken in the hybridization of road vehicles, with the aid of renewables and energy recovery/saving systems. In this context, this paper presents a comprehensive literature review of modern green technologies for GHG reduction that are applicable to road transportation, such as on-vehicle energy harvesting and recovery (e.g., thermal, kinetic, etc.) systems and the incorporation of RES into EV charging stations. The impact of road vehicles on the environment is discussed in detail, along with the EU roadmap towards the decarbonization of transportation. Next, methods and techniques for fuel consumption and GHG reduction are systematically presented and categorized into on-vehicle and off-vehicle ones. Finally, a future outlook on more environmentally friendly road transportation is presented.