Research

37 pages, 9552 KiB

Open AccessArticle

Aspects Regarding of Passive Filters Sustainability for Non-Linear Single-Phase Consumers

by Corina Maria Diniș, Gabriel Nicolae Popa, Corina Daniela Cunțan and Angela Iagăr

Sustainability 2024, 16(7), 2776; https://0-doi-org.brum.beds.ac.uk/10.3390/su16072776 - 27 Mar 2024

Viewed by 397

The efficient use of electrical energy (an important component of sustainability) has become increasingly important for electrical consumers (industrial and non-industrial) as we face the challenges of climate change and the need to protect the environment. This theme is essential for guaranteeing a [...] Read more.

The efficient use of electrical energy (an important component of sustainability) has become increasingly important for electrical consumers (industrial and non-industrial) as we face the challenges of climate change and the need to protect the environment. This theme is essential for guaranteeing a secure and sustainable future for both present and future generations. The power quality and the efficiency of electrical energy are connected to each other. Some power quality problems are caused by natural and unpredictable events, but many disturbances affecting power quality are caused by suppliers and consumers. One of the most important parameters in power engineering is the power factor, which indicates the degree of efficient use of electrical energy. Harmonics is the most important dynamic component of power quality, which affects the operation of electrical equipment and, at the same time, reduces the power factor. Harmonic sources in power systems are generally associated with nonlinear loads. To analyze the operating of passive filters (series L, shunt LC, T type LCL), two groups of experiments (relevant consumers were chosen for the industry as well as from the household sector) were carried out with single-phase nonlinear consumers: in the first group of experiments, a variable-frequency drive is used to supply a three-phase induction motor with variable load; in the second group of experiments, compact fluorescent lamps and LED lamps were used. Following the experiments, it was found that the difficulty of calibrating coils (to size a filter), especially the coils with a core, and the change in electrical properties over time for capacitors. For a certain type of consumer, the improvement of the current waveform depends on the type of filter used, the possibility of improving the power factor (to use electrical energy efficiently), and the role of the source impedance, which is particularly important to improve the efficiency of passive filters. Through the appropriate choice of the passive filter, a decrease in the deforming regime is obtained, with a slight decrease in the active power, and by increasing the power factor, a decrease in the losses of electrical energy from the electrical networks is obtained, with direct implications for the emission of greenhouse gases. Full article

(This article belongs to the Special Issue Critical Issues in Power Engineering and Renewable Energy Technologies)

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13 pages, 14751 KiB

Open AccessArticle

Probabilistic Load Flow Analysis Using Nonparametric Distribution

by Li Bin, Rashana Abbas, Muhammad Shahzad and Nouman Safdar

Sustainability 2024, 16(1), 240; https://0-doi-org.brum.beds.ac.uk/10.3390/su16010240 - 27 Dec 2023

Cited by 2 | Viewed by 714

Abstract

In the pursuit of sustainable energy solutions, this research addresses the critical need for accurate probabilistic load flow (PLF) analysis in power systems. PLF analysis is an essential tool for estimating the statistical behavior of power systems under uncertainty. It plays a vital [...] Read more.

In the pursuit of sustainable energy solutions, this research addresses the critical need for accurate probabilistic load flow (PLF) analysis in power systems. PLF analysis is an essential tool for estimating the statistical behavior of power systems under uncertainty. It plays a vital part in power system planning, operation, and dependability studies. To perform accurate PLF analysis, this article proposes a Kernel density estimation with adaptive bandwidth for probability density function (PDF) estimation of power injections from sustainable energy sources like solar and wind, reducing errors in PDF estimation. To reduce the computational burden, a Latin hypercube sampling approach was incorporated. Input random variables are modeled using kernel density estimation (KDE) in conjunction with Latin hypercube sampling (LHS) for probabilistic load flow (PLF) analysis. To test the proposed techniques, IEEE 14 and IEEE 118 bus systems are used. Two benchmark techniques, the Monte Carlo Simulation (MCS) method and Hamiltonian Monte Carlo (HMC), were set side by side for validation of results. The results illustrate that an adaptive bandwidth kernel density estimation with the Latin hypercube sampling (AKDE-LHS) method provides better performance in terms of precision and computational efficiency. The results also show that the suggested technique is more feasible in reducing errors, uncertainties, and computational time while depicting arbitrary distributions of photovoltaic and wind farms for probabilistic load flow analysis. It can be a potential solution to tackle challenges posed by sustainable energy sources in power systems. Full article

(This article belongs to the Special Issue Critical Issues in Power Engineering and Renewable Energy Technologies)

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22 pages, 4658 KiB

Open AccessArticle

Blockchain-Based Renewable Energy Certificate Trade for Low-Carbon Community of Active Energy Agents

by Shengcheng Fu, Yaxin Tan and Zhiyu Xu

Sustainability 2023, 15(23), 16300; https://0-doi-org.brum.beds.ac.uk/10.3390/su152316300 - 25 Nov 2023

Cited by 1 | Viewed by 1154

Abstract

The future distribution grid is a peer-to-peer (P2P) community formed by a large number of active energy agents (AEAs), and renewable energy certificate (REC) trading is an efficient way to realize a low-carbon AEA community. AEAs can trade not only electricity but also [...] Read more.

The future distribution grid is a peer-to-peer (P2P) community formed by a large number of active energy agents (AEAs), and renewable energy certificate (REC) trading is an efficient way to realize a low-carbon AEA community. AEAs can trade not only electricity but also RECs among themselves to economically and efficiently meet the renewable portfolio standard (RPS) requirements. Aiming to lower the market barrier and increase the trading benefits for market participants, this paper proposes a blockchain-based renewable energy certificate (BCREC) that supports divisible and multiple transactions. The trade process includes four stages: setup, pre-transaction, transaction, and post-transaction. A scheme based on blockchain oracles and smart contracts is implemented to achieve decentralized BCREC issuance and transaction and to support a more flexible trading market. By exploring two typical market scenarios, we verify the advantages of BCREC trading and evaluate its impacts on AEA profits and market efficiency. Full article

(This article belongs to the Special Issue Critical Issues in Power Engineering and Renewable Energy Technologies)

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20 pages, 4597 KiB

Open AccessArticle

A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors

by Xinghua Wang, Xixian Liu, Fucheng Zhong, Zilv Li, Kaiguo Xuan and Zhuoli Zhao

Sustainability 2023, 15(20), 15007; https://0-doi-org.brum.beds.ac.uk/10.3390/su152015007 - 18 Oct 2023

Viewed by 839

Abstract

Under the background of large-scale PV (photovoltaic) integration, generating typical operation scenarios of power systems is of great significance for studying system planning operation and electricity markets. Since the uncertainty of PV output and system load is driven by weather factors to some [...] Read more.

Under the background of large-scale PV (photovoltaic) integration, generating typical operation scenarios of power systems is of great significance for studying system planning operation and electricity markets. Since the uncertainty of PV output and system load is driven by weather factors to some extent, using PV output, system load, and weather data can allow constructing scenarios more accurately. In this study, we used a TimeGAN (time-series generative adversarial network) based on LSTM (long short-term memory) to generate PV output, system load, and weather data. After classifying the generated data using the k-means algorithm, we associated PV output scenarios and load scenarios using the FP-growth algorithm (an association rule mining algorithm), which effectively generated typical scenarios with weather correlations. In this case study, it can be seen that TimeGAN, unlike other GANs, could capture the temporal features of time-series data and performed better than the other examined GANs. The finally generated typical scenario sets also showed interpretable weather correlations. Full article

(This article belongs to the Special Issue Critical Issues in Power Engineering and Renewable Energy Technologies)

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15 pages, 19573 KiB

Open AccessArticle

Classification of 3D Casting Models for Product Lifecycle Management and Corporate Sustainability

by Tzung-Ming Chen, Jia-Qi Wu and Jian-Ting Lin

Sustainability 2023, 15(17), 12683; https://0-doi-org.brum.beds.ac.uk/10.3390/su151712683 - 22 Aug 2023

Cited by 1 | Viewed by 687

Abstract

The purpose of this study was to combine simulations and experiments in order to present the first stage of construction in product lifecycle management. Based on the simplification of casting models, the relationship between the filling and solidification characteristics, casting methods, and geometrical [...] Read more.

The purpose of this study was to combine simulations and experiments in order to present the first stage of construction in product lifecycle management. Based on the simplification of casting models, the relationship between the filling and solidification characteristics, casting methods, and geometrical classifications of aluminum alloy precision casting products was investigated. By rearranging and summarizing the data, the casting models could be digitally managed; moreover, the digitized data could be used as the basis for intelligent processes in further developments. The simulations calculated and analyzed the casting speeds, defect locations, material densities, and critical fraction of a solid A356 aluminum–silicon alloy; the actual casting was carried out and samples were taken for metallographic observation to confirm the simulation results. The part model was simplified with four basic geometric shapes: solid cylinder, tubular, block rectangle, and thin-shell rectangle. The 150 casting models were summarized using 37 combinations, which were further classified into five main categories to match the casting method: solid cylindrical, tubular, and thin-shell rectangular for side casting, and discoidal and plate rectangular for bottom casting. File-compression rates of up to 75% were achieved after classification and archiving, and data integrity was maintained. Finally, model training using random forest classification resulted in an 88.8% accuracy when predicting the casting method. This research is based on the practical issues raised by business owners and R&D engineers, and a solution was obtained. From the perspective of product lifecycle management, the results of this study show the consistency and uniformity of product design rules, as well as the reusability of product process planning, which can be integrated with carbon emissions trading and carbon taxes to save energy and achieve corporate sustainability. Full article

(This article belongs to the Special Issue Critical Issues in Power Engineering and Renewable Energy Technologies)

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19 pages, 2833 KiB

Open AccessArticle

Sustainable Intelligent Energy Management System for Microgrid Using Multi-Agent Systems: A Case Study

by Meryem Hamidi, Abdelhadi Raihani and Omar Bouattane

Sustainability 2023, 15(16), 12546; https://0-doi-org.brum.beds.ac.uk/10.3390/su151612546 - 18 Aug 2023

Cited by 7 | Viewed by 1177

Abstract

In this paper, a sustainable, intelligent energy management system for a microgrid based on a multi-agent system (MAS) is studied. The system is designed to address the challenges posed by the intermittence of renewable energy sources. Also, the system optimizes the use of [...] Read more.

In this paper, a sustainable, intelligent energy management system for a microgrid based on a multi-agent system (MAS) is studied. The system is designed to address the challenges posed by the intermittence of renewable energy sources. Also, the system optimizes the use of available AC–DC renewable energy sources by utilizing load flexibility and the complementarity of renewable sources. To evaluate the effectiveness of this proposed multi-agent framework, a co-simulation using MATLAB and JADE platforms is conducted for a microgrid connected to the main grid. The results show that the proposed energy sharing system achieved over 82.34% of energy savings. This innovative solution has the potential to reduce the need for energy storage and improve energy efficiency while also reducing CO₂ emissions. It offers a promising sustainable development solution for managing and controlling MG. The proposed system contributes to the development of sustainable energy systems based on artificial intelligence to meet the global goals. Full article

(This article belongs to the Special Issue Critical Issues in Power Engineering and Renewable Energy Technologies)

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25 pages, 3821 KiB

Open AccessArticle

Constructing and Validating Professional Competence Indicators for Underwater Welding Technicians for Offshore Wind Power Generation in Taiwan

by Chin-Wen Liao, Kai-Chao Yao, Chin-Tang Tsai, Jing-Ran Xu, Wei-Lun Huang, Wei-Sho Ho and Yu-Peng Wang

Sustainability 2023, 15(14), 10801; https://0-doi-org.brum.beds.ac.uk/10.3390/su151410801 - 10 Jul 2023

Cited by 1 | Viewed by 1262

Abstract

This study aims to develop professional competence indicators for underwater welding technicians for offshore wind power generation in Taiwan. A literature analysis methodology was employed to gather and investigate research studies related to competence indicators in the underwater welding domain of offshore wind [...] Read more.

This study aims to develop professional competence indicators for underwater welding technicians for offshore wind power generation in Taiwan. A literature analysis methodology was employed to gather and investigate research studies related to competence indicators in the underwater welding domain of offshore wind power generation. Subsequently, the Delphi method was utilized to conduct a three-round questionnaire survey, aiming to seek expert opinions regarding the appropriateness and differentiation of these competency indicators. To examine the consistency and significance of expert opinions, the data were subjected to K–S single-sample analysis and K–W one-way analysis of variance. The study identified three main dimensions of professional competency indicators for underwater welding technicians in offshore wind power generation: professional skills, professional knowledge, and workplace attitudes. These dimensions further led to the identification of 10 sub-dimensions, including equipment operation, welding practice, welding inspection, metal materials, welding graphics, occupational safety, quality standards, process improvement, self-management, and teamwork. These sub-dimensions further informed the identification of 75 specific behavioral components as criteria. This study provides findings to enhance future staff training and talent recruitment, benefiting relevant units and managers. These results contribute to enhancing the competence and performance of personnel in underwater welding for offshore wind power generation. Full article

(This article belongs to the Special Issue Critical Issues in Power Engineering and Renewable Energy Technologies)

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17 pages, 5750 KiB

Open AccessArticle

Implementation of EDGE Computing Platform in Feeder Terminal Unit for Smart Applications in Distribution Networks with Distributed Renewable Energies

by Hsin-Ching Chih, Wei-Chen Lin, Wei-Tzer Huang and Kai-Chao Yao

Sustainability 2022, 14(20), 13042; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013042 - 12 Oct 2022

Cited by 4 | Viewed by 1277

Abstract

Under the plan of net-zero carbon emissions in 2050, the high penetration of distributed renewable energies in distribution networks will cause the operation of more complicated distribution networks. The development of edge computing platforms will help the operator to monitor and compute the [...] Read more.

Under the plan of net-zero carbon emissions in 2050, the high penetration of distributed renewable energies in distribution networks will cause the operation of more complicated distribution networks. The development of edge computing platforms will help the operator to monitor and compute the system status timely and locally, and it can ensure the security operation of the system. In this paper, a novel EDGE computing platform that is implemented by a graphics processing unit in the existing feeder terminal unit (FTU) is proposed for smart applications in distribution networks with distributed renewable energies and loads. This platform makes timely forecasts of the feeder status for the next seven days in accordance with historical weather, sun, and loading data. The forecast solver uses the machine learning long short-term memory (LSTM) method. Thereafter, the power calculation analyzers transform feeder topology into the circuit model for transient-state, steady-state, and symmetrical component analyses. An important-factor explainer parses the LSTM model into the concise value of each historical datum. All information transports to remote devices via the internet for the real-time monitor feature. The software stack of the EDGE platform consists of the database archive file system, time-series forecast solver, power flow analyzers, important-factor explainer, and message queuing telemetry transport (MQTT) protocol communication. All open-source software packages, such as SQLite, LSTM, ngspyce, Shapley Additive Explanations, and Paho-MQTT, form the aforementioned function. The developed EDGE forecast and power flow computing platform are helpful for achieving FTU becoming an Internet of Things component for smart operation in active distribution networks. Full article

(This article belongs to the Special Issue Critical Issues in Power Engineering and Renewable Energy Technologies)

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