Use Of Power Research Articles

Power Flow Control with a Single Stage Grid Connected Pv Mechanism with a Multilevel Inverter

There is potential to feed excess electricity to the grid for more efficient and cost-effective power utilization when solar PV arrays are installed widely in comparison to other renewable power output. The primary objective of this article is to use a multilevel inverter to operate autonomously regulate the active and reactive power flow. A vector control system that rotates synchronously with the grid frequency has been devised in both direct and quadrature reference frames. Two current control loops have been implemented to control the current delivered to the grid so that it will has low total harmonic distortion and is in phase with the grid voltage. To run the inverter at a suitable DC link voltage which corresponds to maximum power-which is established by the MPPT algorithm, a DC voltage control loop has also been implemented. Inverters that are connected to the grid are commonly utilized to enhance dispersed generation and power eminence.

Generalized Distribution Network Data-Gathering Procedure for ADMS Deployment

The implementation of advanced distribution management systems (ADMS) in today’s distribution networks (DNs) is critical for efficient operation. However, ADMS deployment poses significant challenges, particularly in gathering the extensive and diverse data required to model DNs. This paper presents a generalized, systematic, and algorithm-driven procedure for optimizing the missing data-gathering process during ADMS deployment. The procedure identifies the required DN model data by layers, considers distribution power utility (DPU) data sources, identifies missing data, and evaluates methods and the missing data-gathering ways, considering cost, duration, and specific constraints for data gathering. The developed approach provides DPUs with a clear, structured, and proactive approach to data gathering, significantly reducing complexity and enhancing efficiency. The practical application of this procedure is demonstrated using a real-world unbalanced DN example from a North American DPU, showcasing its potential to streamline ADMS deployment and deliver tangible operational benefits.

Optimal power utilization in hybrid microgrid systems with IoT-based battery-sustained energy management using RSA-PFGAN approach

The quantity of power electronics converters that interface with the various components of a hybrid microgrid system has a major impact on its efficiency. Minimizing power conversion stages and increasing system efficiency requires integrating a photovoltaic system with micro grids while maximizing the number of converters. This paper presents a hybrid approach for utilizing power in microgrid system with an Internet of Things (IoT) based battery sustained energy management scheme. The proposed hybrid technique combines the Reptile Search Algorithm (RSA) and Progressive Fusion Generative Adversarial Network (PFGAN). Thus, it is referred to as the RSA-PFGAN technique. The principal aim of the proposed approach is to minimize operating costs, improve voltage profiles, and reduce computation time and errors. The discharging and charging strategy of the battery is optimized by the RSA approach. The load demand is predicted using the PFGAN approach. Using MATLAB, the proposed method is evaluated and contrasted to other existing methods. The proposed approach determines better outcomes contrasted to existing techniques such as Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO) and Seeker Optimization Algorithm (SOA). The proposed method achieves an efficiency of 85 %, significantly higher than the PSO's 55 %, WHO's 65 %, and SOA's 75 %. Additionally, the proposed approach exhibits a computation time of just 0.21 s, demonstrating its efficiency compared to PSO at 2.95 s, WHO at 0.87 s, and SOA at 0.43 s. These results indicates that the RSA-PFGAN method offers better performance in terms of cost, efficiency, and computation time for hybrid microgrid systems.

Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks

Achieving transportation decarbonization and reducing carbon emissions are global initiatives that have attracted a lot of effort. The use of electric vehicles (EVs) has experienced a significant increase lately, which will have a considerable impact on current power systems. This study develops a framework to evaluate/mitigate the negative impact of increasing EV charging on urban power distribution systems. This framework includes data analytics of actual residential electrical load and EV charging profiles, and the development of optimal EV charging management and AC load flow models using an actual residential power distribution system in Saskatchewan, Canada. We use statistical methods to identify a statistically-extreme situation for a power system, which a power utility needs to prepare for. The philosophy is that if the power system can accommodate this situation, the power system will be stable 97.7% of the time. Simulation results show the house voltage and transformer loading at various EV penetration levels under this statistically-extreme situation. We also identify that the particular 22-house power distribution system can accommodate a maximum number of 11 EVs (representing 50% EV penetration) under this statistically-extreme situation. The results also show that the proposed optimal EV charging management model can reduce the peak demand by 43%. Since we use actual data for this study, it reflects the current real-world situation, which presents a useful reference for power utilities. The framework can also be used to evaluate/mitigate the impact of EV charging on power systems and optimize EV infrastructure development.

Low carbon economic scheduling of integrated energy system with concentrating solar power and multi-stage hydrogen utilization based on ladder-type carbon trading

ABSTRACT As the energy crisis and the greenhouse effect intensify, developing a sustainable and clean energy system is particularly important. How to increase the utilization of clean energy and reduce carbon emissions is a pressing issue. To this end, an integrated energy system (IES) framework incorporating multi-stage hydrogen utilization and concentrated solar power (CSP) has been developed. This study optimizes the scheduling of the IES using a ladder-type carbon trading method to optimize the output of each unit, thereby achieving low-carbon economic operation. The research investigates the conversion and utilization among multiple heterogeneous energy flows, including electricity, heat, gas, and hydrogen, with a focus on the integration of multi-stage hydrogen utilization and CSP heat supply, and the interconnections among different units. Four comparative cases are established to evaluate the proposed IES framework in reducing the totalcost, mitigating renewable energy curtailment, and lowering carbon emissions. The results show that the proposed IES framework presents optimal results, with a total cost reduction of 19.74% and a carbon emission reduction of 36.79 tons, no new energy waste, and high carbon benefits. The proposed IES framework facilitates flexible and efficient distribution of multiple energy types, meeting energy demands while enabling multi-energy complementarity and feasibility.

Real‐Time Energy Management System for a Hybrid Renewable Microgrid System

ABSTRACTThis paper gives a detailed study for the design and implementation of an energy management system (EMS) for a hybrid renewable microgrid system using real‐time software. Microgrids, with their ability to integrate renewable energy sources, face challenges in maintaining stability and reliability. The implemented EMS aimed to maximize the renewable energy sources utilization, including PV and wind power, in conjunction with a battery energy storage system. The objectives of this research included the implementation of an EMS that ensures a reliable and stable operation between the microgrid system and the main grid including the control of charge and discharge of the battery using Typhoon Hardware‐in‐the‐Loop (HIL) software. The simulation results and case studies demonstrated the effectiveness and performance of the developed EMS in managing a hybrid renewable microgrid system. The results also demonstrated that the time of charging was maximized by utilizing a higher power. By doing so, the battery was fully charged in a shorter timeframe. The battery state of charge (SOC) was maintained between the fixed values (20% and 100%) as stated by the algorithm.

Advancing solar thermal utilization by optimization of phase change material thermal storage systems: A hybrid approach of artificial neural network (ANN)/Genetic algorithm (GA)

Solar energy is critical to the global shift towards sustainable and low-carbon energy solutions. Unlike fossil fuels, solar energy produces no emissions during operation, making it a key driver of environmentally friendly power generation. However, the intermittency of solar power remains a challenge, necessitating efficient energy storage systems to ensure a steady supply. Thermal energy storage systems utilizing phase change materials (PCMs) offer a solution by storing excess solar energy and releasing it when needed. This study focuses on enhancing the charging capacity of the PCM within a novel triplex tube heat exchanger (TTHE). The design incorporates bionic-shaped fins to compensate for thermal conductivity within the PCM. Three artificial neural network (ANN) models were employed to estimate the melting duration for the PCM to attain liquid fractions of 0.5, 0.8, and 1. The expansion angle (θ), initial length (D), and length of fin branches (Z) were systematically varied to investigate their influence on heat absorption. The ANN models exhibited high accuracy, with R2 values of 0.998 for the liquid fraction of 0.5, 0.998 for the liquid fraction of 0.8, and 0.996 for the liquid fraction of 1, demonstrating their precise predictive capability. The results revealed that θ significantly reduced melting time, while Z consistently shortened the duration and eventually became the dominant factor. However, an excessive increase in these geometric parameters led to a counterproductive rise in the total melting time. Through optimization using a genetic algorithm, three optimal designs (design 1 (D1), design 2 (D2), and design 3 (D3)) were proposed. Compared to the fin-less TTHE, these designs reduced the full melting time by 71.58 %, 73.41 %, and 73.54 %, respectively. Faster melting improves the system's ability to store and release thermal energy more quickly, enabling more efficient utilization of solar power within the limited period of sunlight availability (usually 4–6 h a day). These findings underscore the potential of ANN-based approaches in optimizing the efficiency of solar energy storage systems.

Integrated utilization of recycled waste concrete powder and waste glass power for preparation of foam ceramics

Using multi-solid wastes to prepare foam ceramics, it can not only recycle a large amount of solid waste, but also obtain products with excellent performance. In this study, foam ceramics were prepared by sintering method using recycled waste concrete powder and waste glass power as main raw material. The effect of foaming agent content on the physical properties and pore structure of foam ceramics were investigated. The matching relationship between the amount of flux and foaming agent was also analyzed. The results revealed that when the flux was 5 g and the foaming agent was 10 g, the foam ceramics showed the best overall performance. The compressive strength, bulk density, water absorption and porosity were 6.43 MPa, 1.10 g/cm3, 2.03 % and 54.35 %, respectively. At this time, the pore size distribution was optimal, and mesopore occupied the vast majority. The fractal dimension and pore shape factor reached the minimum of 1.344 and 1.076. If the foaming agent was too much, the amount of flux should be appropriately increased. When Na2CO3 content were 12 g and 14 g, the borax were at least 7 g and 9 g, respectively. The matching of flux and foaming agent can significantly improve the foaming effect. The experimental results showed that it was feasible to prepare foam ceramics by using recycled waste concrete powder and waste glass power. The prepared foam ceramics can basically reach or even exceed the current mainstream level. In addition, this foam ceramics showed great environmental and economic prospects.

Unobstructive and safe-to-wear watt-level wireless charger

A wearable microgrid that centralizes and distributes harvested energy across different body regions can optimize power utilization and reduce overall battery weight. This setup underscores the importance of developing cable-free wireless power transfer (WPT) systems for mobile and portable devices to eliminate the risks posed by wired connections, especially in dynamic and hazardous environments. We introduce a thin, stretchable, and safe hand band capable of watt-level wireless charging through the widely adopted Qi protocol operating at 130 kHz. The implementation of non-adhesive fabric encapsulation serves to protect the 50-μm-thin spiral copper antenna from mechanical strain, ensuring an overall hand band stretchability of 50%. We also create a stretchable “Ferrofabric”, characterized by a magnetic permeability of 11.3 and a tensile modulus of 75.3 kPa, that provides magnetic shielding for the antenna without compromising wearability. The “Ferrofabric” improves the coil inductance but induces core loss in AC application. By fully understanding and managing loss mechanisms such as the skin effect, proximity effect, core loss, and joule heating, we achieve a wireless charging efficiency of 71% and power delivery of 3.81 W in the kHz frequency range. Our WPT hand band is unobstructive to hand motion and can charge a handheld smartphone as fast as a desktop charger or power a battery-free chest-laminated e-tattoo sensor, with well-managed thermal and electromagnetic safety. Through a holistic electromagnetic, structural, and thermal design, our device culminates in a safe, rugged, and versatile solution for wearable WPT systems.

Innovative Energy Design for Energy Efficient Buildings - An Example from the Bullitt Center

In recent years, the extreme climate caused by global warming has brought serious consequences to the world. In order to alleviate the problem of global warming, many scholars have started to analyse energy-saving green buildings. Taking the Bullitt Center as an example, this paper illustrates the innovative applications of energy-efficient buildings in solar power generation, rainwater collection and utilization, toilet water conservation and resource utilization and room temperature regulation. It focuses on the differences in the use of solar power generation in winter and summer, the process of purification of rainwater into water, the comparison between composting toilets and vacuum toilets and the steps of regulating the room temperature by windows and ground-source heat pumps. This paper also talks about the limitations of these innovative applications. It could probably give reference to the subsequent construction of buildings, thus promoting energy saving and emission reduction in buildings and mitigating global warming.

Enhancing Cognitive Radio Network Through the Novel Optimization Process and Deep Learning Model in the Transverse System

The dynamic distribution of spectrum, made possible by next-generation networks that utilise Cognitive Radio technology, helps to alleviate spectrum shortages. Additionally, these networks may be dynamically operated to save energy. The innovative cloud-sharing-decision-based method for optimising cognitive radio networks' wireless networks is introduced in this research. Optimisation was achieved via 3 key performance indicators: spectrum utilisation, power utilisation, and human revelation. Researcher find the best key performance indicators for a realistic India’s suburban situation. An optimisation technique in the cloud-based design for architecture simultaneously decreases networks power usage for about 27.6%, mean worldwide revelation by 34.4%, & spectrum utilisation for about 34.6% when related to a standard Cognitive Radio net. For the areas of networks power consumption (4.9%), spectrum utilisation (7.4%), and global exposure (4.4%), method outperforms the old design, even in the worst-case optimisation scenario.

GAPS OF INDIAN ELECTRICAL ENERGY SECTOR AND ITS OPTIMAL MITIGATION BY USING OPTIMAL UTILIZATION OF INDIAN RENEWABLE ENERGY POLICY WITH THE HELP OF THE P&O MPPT TECHNIQUE

Developing countries, especially, India, depend importantly on energy sources to perform their country’s industrial and economic activities. Mainly the two factors continuous population growth of the country and the rapid growth rate of industrialization of the country, the gap between electrical energy supply and demand for electrical energy is slowly and consistently increasing. To resolve this unevenness, there is a jointly planned attempt is required to enhance the contribution of renewable energy in the all over mix of energy. Despite these attempts, unexpected surges caused by the sector of domestic energy consumption could lead to important shortfalls between energy requirements at peak demand and available supply in India. As a solution to this challenge, the Union government of India has launched a new policy, named PM SURYA GHAR: MUFT BIJLI YOJANA, targeted to handle this crucial problem head-on. This initiative requires addressing the increasing energy demand by promoting the utilization of solar power in households throughout the length and breadth of the country. With the implementation techniques of Photo voltaic optimization, this analysis explores how such types of strategies can play a significant role in filling the gap between the energy supply-demand in the Indian context. Employing the solar energy potential and optimizing and utilization of solar energy, creates a path to not only meet the enhancing energy requirements but also creates the path for a more environmentally friendly and sustainable energy in India. By integration of innovative technologies and solutions of renewable energy holds the key to ensuring a balanced, sustainable, stable energy supply for the future of India and country’s growth and development.

Proof of Work with Random Selection (PoWR): An Energy Saving Consensus Algorithm with Proof of Work and the Random Selection Function

Bitcoin, which has been used for 13 years, has a role in transactions and investments as a major cryptocurrency. However, as the number of users increases, Bitcoin faces difficulties, such as scalability for transaction throughput and energy-consumption problems due to the concentration of the mining pool. When Bitcoin first started to come out, it began to develop gradually through the mining of individuals. Nevertheless, as the price of the cryptocurrency gradually climbed, large mining corporation groups entered the mining competition with integrated circuit (IC) chips. Consequently, the substantial increase in power consumption is raising concerns regarding energy expenditure. This paper confirms that the verifiable random selection consensus protocol based on proof of work facilitates a fair and efficient system, enabling the participation of numerous individual miners in the mining competition while counteracting the monopolization of the hash rate by large mining corporations, thereby preserving the decentralization of mining. The protocol demonstrates the potential to mitigate substantial energy consumption. Moreover, it embodies features that create barriers to the adoption of high-energy-consuming application-specific integrated circuit equipment, significantly diminishing the principal factors contributing to extensive power utilization.

Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control

This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.

Studies into Ash Deposition Propensities in the Vicinity of Economizer and Air Preheater of Coal-Fired Power Utility

Studies into Ash Deposition Propensities in the Vicinity of Economizer and Air Preheater of Coal-Fired Power Utility

Improving a WRF-Based High-Impact Weather Forecast System for a Northern California Power Utility

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system.

A black-start strategy for active distribution networks considering source-load bilateral uncertainty and multi-type resources✰

The integration of multi-type resources provides new ideas for the black-start of active distribution networks (ADNs). However, the inability to deal with uncertainty will lead to problems such as frequency/voltage crossing limits, scheduling difficulties, and even restoration failures. To this end, an ADN black-start strategy considering source-load bilateral uncertainty and multi-type resources is proposed. The forecast error uncertainties of renewable energy sources (RESs) and loads are characterized in intervals based on Copula theory, which are then introduced into the black-start model of ADNs and solved by the column-and-constraint generation algorithm. Case studies based on the improved IEEE 33-node system indicate that the proposed strategy can effectively cope with source-load bilateral uncertainty and realize robust restoration of ADNs. The system also achieves a 99.97 % RESs consumption ratio and a 67.36 % power utilization ratio of energy storage devices. Compared with existing methods, our strategy can give a more economical and faster restoration scheme while considering safety, which can be deployed in dispatch centers to help operators make full use of existing resources to achieve black-start safely and stably after outages. However, the computational time will increase if it is migrated to grids with large topologies, which needs to be further investigated.

Design and economic analysis of energy storage optimization allocation for campus microgrid

In the twenty-first century, the "double carbon" plan is advancing steadily. Energy saving and emission reduction is one of the key points of the plan. A power grid independent park, if energy storage equipment can be configured, it can greatly improve the park microgrid power utilization, in order to achieve the purpose of energy saving. Therefore, research on the optimization of the proportion of power grid and microgrid in the park and the optimization of the configuration of energy storage devices can greatly improve the energy utilization rate, so as to achieve the significance of environmental protection. After collecting and sorting the daily load, daily wind power generation and 12-month daily wind power generation data of the three parks A, B and C, and processing the data, a linear function model is established. After using linear programming, operations research and genetic algorithm, the research results are obtained when the three parks are combined and equipped with 50kW/100kWh energy storage devices.

Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor

The objective of this article is to develop and analyse a thyristor-controlled transformer with a fixed capacitor for reactive power compensation in power systems. Reactive power compensation is crucial for enhancing the efficiency and stability of power systems by reducing power losses, improving voltage profiles, and minimizing equipment stress. Traditional compensation methods often rely on fixed capacitors, reactors, or static VAR compensators, but these systems lack the flexibility required for dynamic control of reactive power under varying load conditions. The proposed approach integrates a thyristor-controlled transformer with fixed capacitors, allowing for precise, real-time adjustment of reactive power flow. The novelty of this article lies in the hybrid configuration of the thyristor-controlled transformer and fixed capacitor, which provides a cost-effective and robust solution compared to conventional systems. Unlike traditional methods that depend solely on switching capacitors or reactors, the use of thyristors allows for fine-tuning of reactive power, offering improved performance under variable loading conditions without the need for complex control algorithms. This setup enhances the adaptability of reactive power management, thus maintaining optimal power factor and voltage regulation. The findings from the simulation and experimental results demonstrate significant improvements in power factor correction, voltage stabilization, and reduction in harmonic distortion. The proposed system exhibits a faster response time and greater control accuracy compared to existing compensation techniques. These advantages make the thyristor-controlled transformer with a fixed capacitor a promising alternative for power utilities seeking to enhance the operational efficiency and reliability of their networks. This article contributes to the advancement of reactive power compensation technologies, providing a scalable solution suitable for modern power system.

Data-Informed Risk Analysis of Power Grids: Application of Method for Managing Heterogeneous Datasets

Abstract Power utilities are continuously under high pressure to ensure the best performance of their grid. Nevertheless, power outages continue to be periodically observed. This paper assesses the applicability and implications of the Three-Phases method for optimized dataset selection in dynamic risk analysis, through a case study focusing on vegetation along power lines—a major hazard in power grid management. The case study comprises 17 different real-world datasets originating from 12 different types of data sources. We estimate how these datasets can inform eight parameters related to the physical configuration—one of the three dimensions impacting the probability of tree falls on power lines. The results provide two main take-aways: (1) datasets initially considered as less valuable for risk analysis can end up being the most relevant ones; (2) the potential of knowledge of a dataset needs to be assessed parameter per parameter. The results demonstrate that the Three-Phases method is a step toward traceable, data-driven, and dynamic risk analyses of power grids, resulting in a more reliable management of those large-scale infrastructures.