Energy Resources Research Articles

A framework for developing an attitude determination and control system simulator for Cubesats: Processor-in-loop testing approach

The outbreak of miniaturized satellites is increasingly demanding well-performing attitude determination and control systems (ADCS). Testing an ADCS directly using hardware is extremely costly and risky, whereas testing it using only computer simulation does not yield realistic enough results. Therefore, a detailed framework for developing an ADCS simulator with processor-in-the-loop testing for low-earth orbit Cubesats is established in this paper. In particular, the study focuses on the implementation of the algorithms on a STM32 with ARM Cortex CPU for efficient testing and prototyping. This is done by formulating a quaternion-based satellite dynamic model equiped with a three-axis magnetorquers and a set of pyramidal cluster of reaction wheels for high pointing capabilities. Prominently used sensors are modeled so that the attitude can be estimated using a multiplicative extended Kalman filter. For attitude control, several modes are developed based on different mission stages, and a simplified less burdensome control switching strategy is proposed to maintain stability with minimal computation and energy resources. To test the applicability of the simulator, an ADCS is designed to meet the requirements of the recent low-atmosphere measurement missions. The results are analyzed in accordance with the ESA ECSS standards, and the assessment shows that the specifications can be met with a safe margin, leaving room for unmodeled errors to be taken into account in later stages of the design. Additionally, the proposed control strategy gives various possibilities for missions with different types of objectives, furthermore the framework's flexibility provides easy modification of any part for the user's specific needs.

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

In the dynamic landscape of renewable energies, microgrid systems emerge as a promising avenue for fostering sustainable local energy generation. However, the effective management of energy resources holds the key to unlocking their full potential. This study assumes the task of creating a multi-objective optimization algorithm for microgrid energy management. At its core, the algorithm places a premium on seamlessly integrating renewable energy sources and orchestrating efficient storage coordination. Leveraging the prowess of a multi-agent system, it allocates and utilizes energy resources. Through the combination of renewable sources, storage mechanisms, and variable loads, the algorithm promotes energy efficiency and ensures a steady power supply. This transformative solution is underscored by the algorithm's remarkable performance in practical simulations and validations across diverse microgrid scenarios, offering a prevue into the future of sustainable energy utilization.

Addressing the Water-Energy-Food Nexus in the context of climate change: the case of Bagré dam in Burkina Faso

The water-energy-food (WEF) nexus concept highlights the interdependencies between water, energy, and food resources. Assessing these interdependencies can lead to a better understanding and their exploitation to improve people's access to water, energy, and food. This study aims to highlight the links between the water resource and the current and future water requirements for agricultural and hydropower production in the context of climate change. The bagré dam which is a multi-usage reservoir located in Burkina Faso is considered as the case study. This dam is used for agricultural production, livestock, and hydroelectricity production. To achieve this objective, the surface water resources of the study area and the different water uses have been first estimaded. Then, a model has been developed and implemented using the Water Evaluation And Planning (WEAP) tool, with the reference year 2020. Finally, the developed model was used to study four relevant plausible scenarios in the Bagré area up to 2050, namely: (1) expansion of irrigated schemes, (2) the drinking water needs of a neighbouring town, (3) climate change effects on the dam's water capacity, and (4) combinaison of the three other scenarios. The results show that all the water requirements for different uses are fully met for all the scenario except the combined scenario by 2050. For this last scenario, the unmet water demands reach up to 100% specially during dry season. So, to improve the coverage rate of water demands for the combined scenario, it is crucial to explore other alternatives such as the use of groundwater and the implementation of best practices in agronomic management.

Study the impact of renewable and non-renewable energy sources on micro-grid using time series data based information transfer

This paper addresses the challenges arising from the integration of renewable energy sources into microgrids, focusing on the impact on system dynamics and stability. Employing an advanced time series-based information transfer approach, the study identifies critical parameters influencing system instability under small perturbations. Unlike traditional methods, this technique effectively analyzes information flow and interactions within the complex system, providing insights into the influence of states on different operating modes. The study specifically investigates the disturbance caused by increased load demand and employs a three-layered small signal stability index to pinpoint crucial system-affecting parameters. Subsequently, Distributed Generation (DG) units are strategically placed to restore stability, guided by the identified critical parameters. The proposed approach is validated on IEEE 3 Bus and IEEE 39 Bus systems, demonstrating its effectiveness and outperforming existing methodologies in predicting and addressing system uncertainties. These systems experienced instability as a result of an introduced disturbance in the form of increased load, affecting the dynamics of the generator at the immediate bus. To enhance the generator dynamics and address the impact of the disturbance, a new DG unit was introduced to provide support. The DG unit was placed at the bus which was deduced to be most critical from the three-layered small signal stability.

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.

Validation and test of a novel multi-input converter in extreme conditions using PSOPIC in hybrid power generation environment

The need for renewable energy resources increases due to the day-by-day increase in load demand. Still, there is a need for new technology to operate the existing power system optimally. Distributed generation (DGENs) are helpful in meeting the demand of power due to its lower cost compared to the construction of the complete power system. But this DGEN is constructed using renewable resources, which are intermittent in nature. So, hybrid power generation, which uses multiple sources, is used to satisfy the power need. In this paper, validation, and test of a new multi-input single ended primary inductor converter (MI-SEPIC) is proposed. The performance of the MI-SEPIC converter is tested by connecting photovoltaic (PV), wind, and fuel cells. The proposed system is connected to the grid, and the power transients are analyzed. The DQ control of grid synchronization is discussed in this paper. The conventional PI controller is replaced with a hybrid particle swarm optimization tuned proportional integral control (PSOPIC), and the results are compared. Verification is done using MATLAB software. Validation and test with different test cases to prove the sturdiness of the complete system is explained.

Percentage of Islanding and Peninsulating Detection in ‎Large Microgrids with Renewable Energy Resources ‎with Multiple Connection Points to Different Grids

Percentage of Islanding and Peninsulating Detection in ‎Large Microgrids with Renewable Energy Resources ‎with Multiple Connection Points to Different Grids

Innovative Hierarchical Control of Multiple Microgrids: Cheetah Meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques.This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights.HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities.The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600V-bus According to IEEE519-2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 milliseconds. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility

Explaining the environmental efficiency capability of energy mix innovation among the Nordic countries

Improving the environmental efficiency performance of energy mix is important to the global carbon neutrality goal. Given this motivation, this study delves into providing more explanation about the role of innovation approaches to coal, oil, and natural gas energy resources in the Nordic economies i.e., Denmark, Finland, Norway, and Sweden during the period 2000 to 2019. To achieve this objective and reach an evidence-based insight for policy formulation, the present study leveraged robust panel econometrics estimators that circumvent cross-sectional dependence and heterogeneity issues in the investigated blocs. The empirical evidence shows that economic growth (GDP), coal energy, oil and natural gas contribute to the Nordic economies' environmental degradation in the short- and long-run. Contrarily, innovation via environmental-related patents mitigates environmental degradation, especially in the long run. Furthermore, the interaction models, i.e., applying innovative approaches in the energy mix's consumption, can mitigate carbon emissions across the countries, thus significantly improving environmental sustainability. Specifically, innovative approaches in coal, oil, and natural gas consumption improve environmental sustainability by mitigating carbon emissions, thus delivering respective environmental efficiency improvements of ~94%, ~108%, and ~123%. Additionally, Granger causality evidence further suggests that the examined factors are significant drivers of the environmental indicator. Regarding policy insight, this study affirms that further improvement in energy savings is vital to continuously deploying the conventional energy mix.

Effective Hybrid Deep Learning Model of GAN and LSTM for Clustering and Data Aggregation in Wireless Sensor Networks

Background: Wireless Sensor Networks (WSNs) have emerged as a crucial technology for various applications, but they face a lot of challenges relevant to limited energy resources, delayed communications, and complex data aggregation. To address these issues, this study proposes novel approaches called GAN-based Clustering and LSTM-based Data Aggregation (GCLD) that aim to enhance the performance of WSNs. Methods: The proposed GCLD method enhances the Quality of Service (QoS) of WSN by leveraging the capabilities of Generative Adversarial Networks (GANs) and the Long Short-Term Memory (LSTM) method. GANs are employed for clustering, where the generator assigns cluster assignments or centroids, and the discriminator distinguishes between real and generated cluster assignments. This adversarial learning process refines the clustering results. Subsequently, LSTM networks are used for data aggregation, capturing temporal dependencies and enabling accurate predictions. Results: The evaluation results demonstrate the superior performance of GCLD in terms of delay, PDR, energy consumption, and accuracy than the existing methods. Conclusion: Overall, the significance of GCLD in advancing WSNs highlights its potential impact on various applications.

The weight of data: an analysis based on the impact on the environment

The carbon footprint generated by the information and communications technology (ICT) sector is increasingly significant, emitting greenhouse gases due to high energy consumption, regardless of the way in which energy is generated, the expansion and growth in data centers, as well as the impact generated by the cryptocurrency sector that in the end represents is reflected in greater consumerism, processing, storage, and transport of information that will be somewhere in the world. Current research addresses the problems and the contrast of figures in energy consumption due to the use of a computer, data processing, the role of the user as an internet consumer, the impact of data centers both in carbon footprint, water footprint and soil footprint, the impact of cryptocurrency mining and its contribution to global energy expenditure as well as the ethical debate of new technologies. And finally, the advances in seeking to optimize energy resources, sustainable and conscious for both consumers and service providers, show the trends focused on energy optimization through software and hardware based on a judicious review of research documents.

A Novel Approach for Enhanced Real-Time Event Diagnosis for Grid Connected Microgrid with Multiple Distributed Energy Resources (DERs)

A Novel Approach for Enhanced Real-Time Event Diagnosis for Grid Connected Microgrid with Multiple Distributed Energy Resources (DERs)

Enhancing photovoltaic system maximum power point tracking with fuzzy logic-based perturb and observe method

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems.

Economic optimization of hybrid renewable energy resources for rural electrification

In rural areas, grid expansions and diesel generators are commonly used to provide electricity, but their high maintenance costs and CO2 emissions make renewable energy sources (RES) a more practical alternative. Traditional methods such as analytical, statistical, and numerical-based techniques are inadequate for designing an energy-efficient RES. Therefore, this study utilized the bat algorithm (BA) to optimize the use of hybrid RES for rural electrification. A feasibility study was conducted in the village of Kalema to assess energy consumption, and a diesel-only system was modeled to serve the entire community. The BA was used to determine the optimal size and cost-effectiveness of the hybrid RES, with MATLAB R (2021a) utilized for simulation. The BA's performance was compared with diesel only and GA using cost of energy (COE) and CO2 emissions as metrics. Diesel generators only produced a COE of $6,562,000 and 1679.6 lb/hr of CO2 emissions. COE with BA was $356,9781.37 (a 45.6% reduction) and CO2 emissions were 635.29 lb/hr (a 62.2% drop). Genetic algorithm (GA) resulted in $364,3122.46 COE and 652.69 lb/hr CO2 emissions, indicating 61.1% and 44.5% decreases, respectively. BA significantly reduced COE and CO2 emissions over GA, according to the analysis.

A differential current based protection scheme for DC microgrid

A continuous increase in greenhouse emissions has led to more frequent use of renewable energy resources. The increased emergence of DC loads in day-to-day lives has further led to conversion to DC distribution lines. In prospect of getting more environmentally friendly, economical, and reliable power delivery a DC micro grid was developed and has become more common in recent years. The disaster management cell located at the Department of Electrical and Electronics Engineering has designed a DC micro grid which consists of sources provided from the grid, a battery bank, and an array of solar cells supplying to a set of nine loads, which are segregated into three sets of three loads each. This paper presents a protection scheme for the buses present in a micro grid that is based on the differential current principle. It is done with the help of a centralized protection controller that enables fault identification and fault isolation. This protection scheme is further extended to a DC ring-bus micro grid, where the centralized protection controller enables fault identification, fault location, and fault isolation. MATLAB/Simulink is used to obtain the simulation and verify the results.

A review of the state of art and prospects in energy storage systems for energy harvesting applications

Due to the increasing trend in worldwide energy consumption, many new energy technology systems have emerged in the past decades. The implementation of energy storage system (ESS) technology in energy harvesting systems is significant to achieve flexibility and reliability in fulfilling the load demands. In this paper, several types of energy storage technologies available in the market are discussed to view their benefits and drawbacks. The main aim of this review is to provide a platform for readers especially those who seek to know more about ESS at a glance, to decide which ESS technology is best suited for any specific applications. This review would serve as a base for the initial state to make the right decision by referring to the criterias and characteristics of energy resources to get the optimal ESS technology. A comprehensive comparison among the various types of ESS technologies is outlined and elaborated to provide a better and clearer picture to the readers. Last but not least, the relevant recommendations and alternative choices for services related to the harvesting of solar PV energy are described too. It is hoped that the findings of this review article may be helpful to all readers interested in ESS technology.

Blockchain based sustainable energy transition of a Virtual Power Plant: Conceptual framework design & experimental implementation

Sustainable frameworks are being introduced by technological breakthroughs to address the continuously increasing demand for energy and this article presents a novel framework for a Virtual Power Plants to sustain and optimize Distributed Energy Resources (DERs) administration through blockchain technology. The multi-layered architecture integrates blockchain technology with other VPP components, such as a cloud service provider, grid and transmission operator, and forecasting of DERs, to optimize bidding and scheduling in real-time across multiple markets to enhance energy efficiency and grid stability. By employing a system engineering methodology, this framework incorporates smart meters to facilitate the transmission and reception of data i.e., voltage, and current between participants, and DERs within the network. This strategy makes it easier to advance sustainable energy sources, which advances predictive capabilities, strengthens the framework’s resilience, and protects the environment. The investigation of demand response, functional and system trade-off analysis, configuration selection, and other subsystems within a decentralized network offers insights into the necessary components and challenges involved in the construction of such a system. This study contributes comprehensive assistance and valuable insights to both practitioners and researchers involved in the field of sustainable energy transition through Virtual Power Plant.

Frequency Stabilizing and Reducing Power Outages of The Islanded Power ‎Network Using a ‎Load Shedding Method in The Presence of Renewable Energy ‎Resources

Frequency Stabilizing and Reducing Power Outages of The Islanded Power ‎Network Using a ‎Load Shedding Method in The Presence of Renewable Energy ‎Resources

Photovoltaic Powered Dual Thermoelectric Air Conditioning System

Abstract: Nowadays energy efficient and environment friendly systems are among the emerging technologies, due to growing concerns about climate change and sustainability. According to the present scenario, the renewable energy resources are becoming the source of energy due to the decrease in non-renewable energy resources (Fossil fuels). So we presented a “Photovoltaic-Powered Dual Thermoelectric Air Conditioning System” that incorporates Peltier modules(thermoelectric material) which works on peltier effect known for their eco-friendly characteristics. Harnessing solar energy through a highefficiency PV array, this system aims to provide efficient and sustainable air conditioning while minimizing the reliance on traditional power sources. The additional benefit of this system is that it provides year round comfort i.e. cooling and heating facility in one system.

Optimal Deployment of Wireless Sensor Nodes using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO)

Abstract: Wireless Sensor Networks (WSNs) are emerging as a significant area of research due to their potential to autonomously monitor physical and environmental conditions. These networks comprise spatially distributed, low-cost sensor nodes with limited transmission range, processing capabilities, storage, and energy resources. The primary function of these networks is to collect data from various nodes and transmit it to a base station for subsequent processing. WSNs present several challenges, including optimal sensor deployment, node localization, base station placement, target node location, energy-aware clustering, and data aggregation. Recently, global researchers have been employing a bio-inspired optimization algorithm, Particle Swarm Optimization (PSO), to enhance the efficiency of WSNs. This report explores the application of the PSO algorithm for optimal sensor deployment in WSNs, contributing to the ongoing efforts to maximize the potential of these networks.