Integration Of Renewable Energy Sources Research Articles

Driving innovation in energy and telecommunications: next-generation energy storage and 5G technology for enhanced connectivity and energy solutions

Driving innovation in energy and telecommunications involves leveraging next-generation energy storage and 5G technology to enhance connectivity and energy solutions. This review explores the intersection of these two domains, highlighting the importance of advancements in energy storage and 5G technology for a sustainable and connected future. Energy storage is crucial for balancing the supply and demand of electricity in modern power systems. Traditional energy storage methods, such as batteries and pumped hydro, have limitations in terms of scalability, efficiency, and cost-effectiveness. Next-generation energy storage technologies, including advanced batteries, hydrogen storage, and thermal storage, offer promising solutions to overcome these limitations. These technologies enable efficient energy storage at scale, facilitating the integration of renewable energy sources like solar and wind into the grid. By storing excess energy generated during periods of low demand, next-generation energy storage systems ensure a reliable and stable power supply, reducing the reliance on fossil fuels and lowering greenhouse gas emissions. In parallel, the evolution of telecommunications technology, particularly the advent of 5G networks, is revolutionizing connectivity and communication. 5G technology offers significantly higher data transfer speeds, lower latency, and increased network capacity compared to its predecessors. These capabilities are essential for supporting emerging technologies such as the Internet of Things (IoT), autonomous vehicles, and smart grids. With 5G-enabled IoT devices, utilities can monitor energy consumption in real-time, optimize grid operations, and detect and respond to faults more efficiently. Moreover, 5G connectivity enhances the efficiency and reliability of energy storage systems by enabling seamless communication between distributed energy resources and grid operators. The convergence of next-generation energy storage and 5G technology presents numerous opportunities for driving innovation in both energy and telecommunications sectors. One of the key areas of innovation is the development of smart energy storage systems equipped with 5G connectivity. These systems can autonomously adjust their operation based on grid conditions, weather forecasts, and energy demand patterns, optimizing energy storage and distribution in real-time. Furthermore, advanced energy management algorithms leveraging artificial intelligence (AI) and machine learning (ML) algorithms can optimize energy usage and storage, further improving the efficiency and reliability of the grid. Another area of innovation lies in the integration of renewable energy resources with 5G-enabled microgrids. Microgrids are localized energy systems that can operate independently or in conjunction with the main grid. By combining renewable energy sources with energy storage and 5G-enabled communication, microgrids can provide reliable, clean, and resilient power to remote or urban areas. These microgrids can also facilitate peer-to-peer energy trading, allowing consumers to buy and sell excess energy within their communities, fostering energy independence and sustainability. Furthermore, advancements in battery technology, such as solid-state batteries and flow batteries, are enhancing the performance and reliability of energy storage systems. Solid-state batteries offer higher energy density, faster charging rates, and improved safety compared to conventional lithium-ion batteries. Flow batteries, on the other hand, provide scalability and long-duration storage capabilities, making them suitable for grid-scale applications. Integrating these advanced battery technologies with 5G-enabled monitoring and control systems enhances the overall resilience and flexibility of the energy infrastructure. In addition to technological advancements, driving innovation in energy and telecommunications requires collaboration among various stakeholders, including policymakers, regulators, industry players, and research institutions. Policies and regulations should incentivize the deployment of next-generation energy storage and 5G infrastructure, promote interoperability standards, and ensure data privacy and security. Public-private partnerships can facilitate the investment and deployment of innovative solutions, while research and development initiatives can spur further technological advancements. Driving innovation in energy and telecommunications through next-generation energy storage and 5G technology is essential for building a sustainable, connected, and resilient future. By leveraging advanced energy storage systems, smart grids, and 5G-enabled communication networks, we can optimize energy usage, reduce carbon emissions, and enhance the reliability and efficiency of our energy infrastructure. Collaboration and investment across various sectors are key to unlocking the full potential of these transformative technologies and achieving a brighter, more sustainable future for generations to come. Keywords: Innovation, Energy, Telecommunications, Next-Generation, 5G technology, Enhanced connectivity.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Advancements and Challenges in Integrating Renewable Energy Sources into Distribution Grid Systems: A Comprehensive Review

Abstract The issues in integrating renewable energy sources (RES) into distribution grid structures are thoroughly examined in this research. It highlights how important this integration is to updating the energy system and attaining environmental goals. The study explores the specific problems confronted by means of on-grid power structures, along with overall performance metrics and compatibility issues. Additionally, it presents a thorough assessment of the attributes for various RES hybrid systems, together with technology from the fields of solar, wind, batteries, and biomass. to be able to spotlight the significance of innovative solutions inside the dispersed technology environment, the integration of RES with CCHP structures is investigated. This study addresses the numerous problems with RES integration into the grid to better comprehend their intricacies. The viability of RES integration is supported by real-world case studies that provide operational examples of dispersed generation systems. The study concludes by discussing the technical, financial, and grid-related problems associated with distributed generating systems' limits and highlighting the contribution of cutting-edge technology and artificial intelligence to their removal. In conclusion, the report highlights the development toward smarter grids and improved distributed generating capacities as the essential component of a robust and sustainable energy future.

Joint energy management with integration of renewable energy sources considering energy and reserve minimization

In light of electric vehicles (EVs) and demand-side management, a combined optimization paradigm for microgrid (MG) resource scheduling is proposed in this study. Renewable energy sources, including wind and solar power, fulfill a fraction of the consumption demand, while the MG maintains a connection to the electrical network to facilitate power transactions. By utilizing EVs, we can mitigate the inherent volatility of renewable energy production with manageable loads and level out the system's demand profile. The proposed system employs a bi-level programming strategy, wherein the initial level is dedicated to the minimization of aggregate costs, encompassing those related to energy and reserves. Additionally, costs associated with resource redistribution due to the uncertainty of solar and wind power output are minimized at the second level. Subsequently, the objective function is addressed using the enhanced adapted approach of modified marine predators algorithm (MMPA), chosen for its robustness and effectiveness as an optimization technique. The outcomes obtained for a microgrid underscore the superior performance of the results achieved through the application of the MMPA algorithm when contrasted with alternative established methodologies. The outcomes have demonstrated that integrating EVs and adaptable loads contributes to a decrease in operational expenses and emissions. Furthermore, the uncertainties associated with WT and PV are effectively counterbalanced. Additionally, the results garnered from the simulations indicate that the recommended MMPA technique has the capacity to yield superior solutions compared to several other widely recognized optimization algorithms. Scheduling with both DR and EVs with three cost components are considered, generation cost, reserve cost, and initial unit cost, amounting to $740.11, $9.61, and $6.07, correspondingly and it can lead to decreased emission levels and increased incentives for proprietors of EVs, responsive load participants, and RES.

Stability and Reactive Power Sharing Enhancement in Islanded Microgrid via Small-Signal Modeling and Optimal Virtual Impedance Control

In the context of integrating Renewable Energy Sources, Microgrid (MG) development is pivotal, particularly as a foundational technology for Smart-Grid evolution. Despite advancements in control techniques, challenges persist in ensuring system stability and accurate power sharing across diverse operational conditions and load types. The objective of this research is to control numerous paralleled inverters-based distributed generators (DGs) that contribute to power sharing in an island MG. The proposed methodology involves developing an innovative small-signal model for islanding MGs that incorporate virtual impedances. Subsequently, optimization algorithms based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are proposed and compared for designing the virtual impedances. These algorithms analyze all potential operating points, aiming to minimize reactive power mismatches while maximizing MG stability. The suggested objective function facilitates the simultaneous achievement of these objectives. The proposed approaches were tested using MATLAB-Simulink software, and the comparison of the results between conventional approach and the proposed optimal approaches shows significant improvement in terms of the dynamic response during load changes, such as a decrease in response time by up to 20%, a reduction in overshoot percentage by approximately 15%, and a settling time improvement of nearly 25%. These quantified improvements highlight the effectiveness of the GA and PSO methods in minimizing the reactive power-sharing error while optimizing MG performance and stability.

State‐of‐the‐art CO2 reduction in electrochemical microfluidic systems: A short review and new perspectives

The need to mitigate carbon dioxide has motivated an influx of interest in innovative technologies, and microfluidic systems have emerged as a promising forefront in this endeavor. This short review analyzes CO2 reduction within microfluidic platforms, thoroughly investigating existing methodologies, challenges, and novel perspectives. This work commences with a detailed exposition of the fundamental principles governing microfluidic electrolyzers, elucidating the interaction of microchannels and electrodes. We show the electrochemical reactions supporting CO2 reduction, detailing the processes at the cathode. The use of microfluidic systems encompasses precise control over reaction conditions, efficient mass transport, reduced energy consumption, high throughput screening capabilities, integration with analytical tools, and portability. Catalyst selection for optimal CO2 reduction products, technical complexities, integration of renewable energy sources, and cost‐effectiveness are notable challenges on the horizon. A perspective on potential pathways to resolution is delineated, with each impediment succinctly but insightfully addressed. Moreover, this review extends beyond a looking‐back analysis, propounding innovative perspectives. It posits the concept of microfluidic fuel cells directly fueled by CO2. This work seeks to inspire new researchers and innovation in the field by comparing the present state‐of‐the‐art with prospective avenues of exploration

Li4SiO4-Based Heat Carrier Derived from Different Silica Sources for Thermochemical Energy Storage

Thermochemical energy storage (TCES) is one of the key technologies facilitating the integration of renewable energy sources and mitigating the climate crisis. Recently, Li4SiO4 has been reported to be a promising heat carrier material for TCES applications, owing to its moderate operation temperature and stability. During the synthetic processes, the properties of the Si source used directly influence the performance of derived Li4SiO4 materials; however, the internal relations and effects are not yet clear. Hence, in this work, six kinds of SiO2 sources with different phases, morphology, particle size, and surface area were selected to synthesize a Li4SiO4-based TCES heat carrier. The physicochemical properties of the SiO2 and the corresponding derived Li4SiO4 were characterized, and the comprehensive performance (e.g., heat storage/releasing capacity, rate, and cyclic stability) of the Li4SiO4 samples was systematically tested. It was found that the silica microspheres (SPs), which possess an amorphous phase, uniform micro-scale structure, and small particle size, could generate Li4SiO4 TCES materials with a highest initial capacity of 777.7 kJ/kg at 720 °C/900 °C under pure CO2. As a result, the SP-L showed an excellent cumulative heat storage amount of 5.84 MJ/kg within 10 heat-releasing/storage cycles, which was nearly 1.5 times greater than the value of Li4SiO4 derived from commonly used silicon dioxide. Furthermore, the effects of the utilized Si source on the performance of as-prepared Li4SiO4 and corresponding mechanisms were discussed, which offers guidance for the future selection of Si sources to produce high-performance Li4SiO4-based TCES heat carriers.

Review on Economic Analysis of Grid-tied Battery Storage Systems and Optimization Strategies

This comprehensive review paper provides an in-depth exploration of the landscape of energy management strategies within the context of emerging energy markets. It underscores the significance of effective energy management and optimization, particularly in light of the surging integration of renewable energy sources, growing sustainability concerns, and the dynamic shifts within the energy sector. This review extensively analyses three fundamental domains: electricity demand estimation, battery storage systems, and energy arbitrage methodologies placing a significant emphasis on the body of the research related to electricity storage optimization.Furthermore, it delves into the widespread strategies that are adopted in battery storage systems, which have assumed a pivotal role in energy storage and the equilibrium of supply and demand in the modern energy landscape. Nevertheless, amidst this extensive examination, a considerable research gap arises, specifically highlighting the lack of dedicated studies addressing energy arbitrage methods tailored to the unique attributes of emerging energy markets. This gap is particularly prominent in the context of Sri Lanka's growing energy market, where the prominence of renewable energy sources is on the rise.The review underscores that in order to fully harness the vast potential of renewable resources and optimize energy supply within this distinctive setting, it is crucial to develop and implement customized energy arbitrage strategies. These strategies should be designed to accommodate the evolving energy dynamics and the integration of renewable energy, that would thus enable energy markets to function effectively while maximizing economic benefits. Therefore, as the global energy landscape continues to transform, with a growing focus on sustainability and renewable energy, this paper sheds light on the need for innovative and tailored energy solutions in emerging markets.

The Promising Potential of Gallium Based Liquid Metalsfor Energy Storage.

Energy storage devices play a crucial role in various applications, such as powering electronics, power backup for homes and businesses, and support for the integration of renewable energy sources into electrical grid applications. Electrode materials for energy storage devices are preferred to have a flexible nature, conductive, better capacity, and low-toxicity. Currently, using Gallium based liquid metal alloys, such as Eutectic Gallium-Indium (EGaIn), Eutectic Gallium-Tin (EGaSn), and Eutectic Gallium-Indium-Tin (EGaInSn), as electrode materials play very important role in energy storage devices. These liquid metals have some interesting properties with a self-healing nature, high mechanical stability, compatibility with various materials, fluidity, low young's modulus, high electrical and thermal conductivity. Those properties have made it suitable to be used in various energy storage devices. In this mini review, we have concisely described the advantages and challenges of using liquid metal as electrode materials for various energy storage devices.

Enhanced EV Battery Monitoring Using IoT with Improved SEPIC - ZETA Converter and Modified Lion Optimization for Photovoltaic Systems

The rising popularity of electric vehicles (EVs) and the increasing emphasis on sustainable transportation have highlighted the importance of advanced monitoring systems. These systems play a crucial role in providing real-time information about battery parameters to ensure optimal performance and longevity. Moreover, the integration of renewable energy sources, specifically photovoltaic (PV) systems, with EV charging infrastructure presents an exciting prospect to enhance energy efficiency and minimize environmental harm. As a consequence the proposed work develops an Internet of Things (IoT) based PV fed EV charging system. The system incorporates an improved SEPIC-Zeta converter together with Modified Lion Optimization algorithm (LOA) assisted Proportional Integral (PI) controller for optimizing PV system for charging EV battery. IoT connectivity monitors parameter such as PV voltage, PV current, and State of Charge (SOC) of battery. Real-time monitoring of these parameters is achieved by leveraging sensors. The collected data is transmitted through IoT networks to a central monitoring system, enabling operators to track the performance of PV system and EV battery. This facilitates optimized energy generation, efficient battery charging and discharging, and overall system reliability. IoT-enabled monitoring system provides remote accessibility, data analytics, and the generation of alerts and notifications for timely actions. Experimental validation demonstrates that, the proposed concept enhances the monitoring capabilities, improves energy efficiency, and ensures reliable operation of EV charging infrastructure.

A Multi-Criteria Decision-Making Model to Determine the Optimal Share of Variable Renewable Energy Sources in a Wholesale Electricity Market

In recent years, the development of renewable energy technologies has gotten increasing attention due to the realization of cleaner production and high energy efficiency. Therefore, the proportion of these resources in electricity production has experienced a dramatic increase. However, with the features of intermittent nature and merit order effects, it is challenging to effectively optimize the integration of renewable energy sources (RES) into the electricity markets. To deal with the complex problem of selecting the proportionate share of non-dispatchable RES for participation in the market, this paper establishes a multi-criteria decision-making (MCDM) framework, which combines the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Five criteria are defined and the corresponding mathematical model is solved by the proposed AHP-SAW method. In this way, the optimal share of renewable resources for participation in the wholesale market can be determined based on the desired criteria. Also, to compare the results obtained by the AHP-SAW method, the criteria weights are determined by Shannon entropy, and the problem is solved by the TOPSIS method. To assert the effectiveness of the proposed framework, two case studies are done. According to the results, by increasing the RES penetration level, the average price of the market decreases mildly while price volatility increases significantly remarkably. Also, the amount of forward contracts is decreased when the RES share increases. Moreover, the results show that a high amount of RES is not necessarily desirable and depends on the type of renewable resources and policymakers' priorities. The results for systems 1 and 2 indicate that the maximum allowable amount of RES are 85.359 and 38.24MW, respectively.

A hybrid islanding detection method using wavelet transform for hybrid systems with zero non-detection zone

Purpose Islanding detection has become a serious concern due to the extensive integration of renewable energy sources. The non-detection zone (NDZ) and system-specific applicability, which are the two major issues with the islanding detection methods, are addressed here. The purpose of this paper is to devise an islanding detection method with zero NDZ and, which will be applicable to all types of renewable energy sources using the sequence components of the point of common coupling voltage. Design/methodology/approach Here, a parameter using the sequence components is derived to devise an islanding detection method. The parameter derived from the sequence components of point of common coupling voltage is analysed using wavelet transform. Various operating conditions, such as islanding and non-islanding, are considered for several test systems to evaluate the performance of the proposed method. All the simulations are carried out in Simulink/MATLAB environment. Findings The results showed that the proposed method has zero NDZ for both inverter- and synchronous generator-based renewable energy sources. In addition, the proposed method works satisfactorily as per the IEEE 1547 standards requirement. Originality/value Performance of the proposed method has been tested in several test systems and is found to be better than some conventional methods.

The integration of solar-hydrogen hybrid renewable energy systems in oil and gas industries for energy efficiency: Optimal sizing using Fick's Law optimisation Algorithm

The global demand for sustainable energy solutions in the oil and gas industry has stimulated interest in the integration of renewable energy sources. This paper investigates the techno-economic and environmental aspects of incorporating a solar-hydrogen-based hybrid renewable energy system (SHRES) into oil and gas processing facilities.The proposed SHRES, comprising Solar PV modules, hydrogen production and storage systems, and a gas turbine generator (PV-FC-EL-Tank-GT), aims to enhance energy efficiency and reduce the carbon footprint of energy-intensive operations. The study utilises a multidisciplinary approach, encompassing engineering, economics, and environmental analysis. The techno-economic evaluation employs detailed modelling and optimisation using Fick's Law Algorithm, ensuring efficient energy production and cost-effectiveness. Ecological assessments quantify potential reductions in greenhouse gas emissions and other environmental impacts.The technical and economic evaluation identifies the electrolyser system as the most significant cost contributor (57%). The optimised system balances energy sources with 45% from PV modules, 35% from fuel cells, and 20% from gas turbines, showcasing substantial reliance on renewable energy while maintaining required power generation.Addressing the urgent need for sustainable energy solutions in the oil and gas industry, the study highlights the potential of SHRES integration. By employing Fick's Law Algorithm, alongside with 80 % of renewable energy integration, the research optimises system components to optimise the annualised cost to be $57,539.85, decrease to zero the Loss of Power Supply Probability (LPSP), and mitigate CO2 emissions to a lowest value of 0.147 gCO2eq/kWh (by 80 %). The electrolyser system's pivotal role and the distribution of power generation sources are emphasised.This study presents a novel approach to integrating solar-hydrogen systems into oil and gas processing facilities, aiming for sustainability. The findings underscore the significance of the electrolyser system and the distribution of power generation sources in optimising the SHRES. Recommendations for renewable energy integration and potential reductions in CO2 emissions and costs are discussed, providing valuable insights for future energy policies.

Optimal Load Shedding Based On Hybrid Energy Generation

The demand for electricity is growing at an exponential rate with no corresponding increase in the electricity production, which has led to voltage collapse in many practical power systems all over the world. This has, therefore, been a serious concern to power system engineers, investors and utilities as its effect is highly detrimental to power system operation and its reliability. Consequently, in the quest for providing solutions to this problem, integration of various renewable energy sources are deployed in recent years. This paper, therefore, presents an effective approach to solving voltage collapse problems through load shedding optimization. HOMER software was deployed due to its great features of quality design and evaluation, coupled with its technicality and economical options for both standalone and grid-connected power system. The results show that the utilization of the hybrid Hydro/PV produces 91kWh annually, whereas 88.5% of the electricity is produced by hydroelectric system while the remaining 11.5% of the electricity comes from the integrated photovoltaic system with excess electricity of 84kwh per year. The results obtained, therefore, show that there is an assurance of stable electricity with the introduction of backup battery banks even with the intermittent nature of the renewable energy systems.

Review of energy-efficient HVAC technologies for sustainable buildings

This review paper provides a comprehensive examination of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) technologies for sustainable buildings. The focus is on exploring the latest advancements, benefits, challenges, and future prospects in the realm of HVAC systems designed to enhance energy efficiency and promote sustainability in building operations. The review begins with an overview of the importance of energy-efficient HVAC technologies in the context of sustainable building design and operation. It underscores the critical role of HVAC systems in reducing energy consumption, minimizing environmental impact, and enhancing occupant comfort and well-being. The paper delves into various categories of energy-efficient HVAC technologies, including advanced heating and cooling systems, ventilation strategies, and smart controls. Each category is examined in detail, highlighting key features, performance metrics, and real-world applications. Furthermore, the review evaluates the benefits associated with energy-efficient HVAC technologies, such as significant energy savings, reduced operating costs, improved indoor air quality, and enhanced building resilience. It also discusses the potential challenges and barriers to adoption, such as high upfront costs, complexity of implementation, and retrofitting existing buildings. The paper explores emerging trends and innovations in the field of energy-efficient HVAC technologies, including the integration of renewable energy sources, smart sensors and actuators, and predictive maintenance algorithms. These advancements have the potential to further optimize energy performance, increase system reliability, and enable proactive management of HVAC systems. In conclusion, the review emphasizes the importance of energy-efficient HVAC technologies as a cornerstone of sustainable building practices. It underscores the need for continued research, innovation, and collaboration among stakeholders to overcome existing challenges and unlock the full potential of energy-efficient HVAC systems in contributing to a more sustainable built environment. Overall, this comprehensive review provides valuable insights into the current state of energy-efficient HVAC technologies, their applications, benefits, and challenges, while also highlighting opportunities for future advancements and their crucial role in achieving sustainability goals in the building sector.

Evaluation of volcanic ash as a low-cost high-temperature thermal energy storage material for concentrated solar power

The integration of renewable energy sources is facilitated by TES because it enables the storage and release of excess clean energy, which improves grid stability. In concentrating solar power plants (CSP), solar molten salt is frequently used since it has some advantages such as good thermal properties. However, certain challenges, such as molten salt corrosion, salt solidification, and high production costs, must be carefully considered. An alternative approach gaining attention involves the use of solid ceramic materials capable of withstanding high temperatures as a potential TES medium without the molten salt drawbacks. Elevating the operating temperature of power conversion processes while concurrently reducing capital costs is a strategic means to enhance the competitiveness of these innovative power plants. This study explores the potential of volcanic ash, a low-cost naturally occurring ceramic material, for TES. The evaluation revealed high-temperature stability up to 750 °C, slight mass gain but stable over time, elevated solar absorption, and excellent thermal and chemical stability, even in the presence of molten salts.

Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine

The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time​ conditions.

Micro Grid Integration of Distributed Power Generation Optimal Operating Methods

Microgrids, as localized energy systems comprising distributed power generation sources, offer resilience, flexibility, and efficiency to the modern power grid. Integrating various distributed energy resources (DERs) within microgrids poses challenges in optimizing their operation to ensure reliable and cost-effective energy supply. This paper presents a comprehensive review and analysis of optimal operating methods for the integration of distributed power generation within microgrids.The study explores various aspects of microgrid operation, including optimal dispatch strategies, demand-side management techniques, energy storage utilization, and renewable energy source integration. It investigates the application of advanced optimization algorithms, such as linear programming, mixed-integer linear programming, and metaheuristic algorithms, to address the complex optimization problems inherent in microgrid operation. Furthermore, the paper examines the role of smart grid technologies, advanced control systems, and communication infrastructure in facilitating the efficient operation of microgrids with distributed power generation. It discusses the importance of real-time monitoring, data analytics, and predictive modeling for enhancing decision-making processes and optimizing microgrid performance.

Big data resolving using Apache Spark for load forecasting and demand response in smart grid: a case study of Low Carbon London Project

Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration, and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters' readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters' data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models' accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results.

Design of adaptive protection coordination scheme using SVM for an AC microgrid

Microgrids have gathered a significant amount of attention within the past decade and becoming an essential asset in the energy industry. The ability to integrate sustainable renewable energy generation into the distribution network is one of the main reasons for microgrids' popularity. Besides technical advantages, renewable energy sources integration with the grid poses many technical challenges. Due to randomness in generation and different modes of operation, integrating renewable energy sources into power systems will be challenging. Reliable operation of renewable energy sources integrated microgrids even when faults occur, requires an adaptive protection scheme and relay coordination. In order to tackle those issues, this paper developed an adaptive protection coordination scheme using numerical overcurrent relays and support vector machines with a particle swarm optimization approach. Renewable energy sources based on microgrids use power electronics systems that require relays to operate in milliseconds, and the power system will be safe. particle swarm optimization's program execution time exceeds the primary relays. The machine learning algorithm is faster in computation time. This article proposes a method, particle swarm optimization, to estimate the optimum relay operating time. The machine learning model having a support vector machine is proposed to estimate the relay operating time. The proposed research is developed and tested on an IEEE12BUS and IEEE35BUS system. The support vector machine -based on adaptive protection coordination schemes estimates the operating time of directional overcurrent relays, with a lesser program execution time and a low percentage error rate. The derived result defined that the proposed approach is well-suitable for real-time applications of an AC microgrid system.