Inventions

19 pages, 7479 KiB

Open AccessArticle

Optimal Scheduling of Virtual Power Plants Under a Multiple Energy Sharing Framework Considering Joint Electricity and Carbon Trading

by Xue Li, Xuan Zhang, Jiannan Zhang, Wenlu Ji, Lifeng Wang, Xiaomin Lu and Jingchen Zhang

Inventions 2024, 9(6), 119; https://doi.org/10.3390/inventions9060119 - 2 Dec 2024

Abstract

The virtual power plant (VPP) is an excellent approach for mitigating the intermittency and fluctuation of renewable energy sources. The present work proposes an optimal scheduling model for VPPs to leverage the benefits of joint electricity and carbon trading from the perspective of [...] Read more.

The virtual power plant (VPP) is an excellent approach for mitigating the intermittency and fluctuation of renewable energy sources. The present work proposes an optimal scheduling model for VPPs to leverage the benefits of joint electricity and carbon trading from the perspective of multiple energy-sharing mechanisms. First, the optimal sharing scheduling model of the electric, thermal, and hydrogen energy was established. The model integrates various components, including wind turbines, photovoltaic units, electrolytic cells, combined heat and power units, hydrogen-doped gas boilers, electric energy storage, thermal storage tanks, and hydrogen storage tanks. Then, the model incorporates a tiered carbon trading mechanism to minimize operating and trading costs. Finally, numerical results indicate that, compared with the independent operation of virtual power plants and the lack of joint electricity and carbon trading, the optimal scheduling scheme proposed in this paper reduces the total cost and carbon emissions of the three VPPs by 3.3% and 49.7%, respectively. This demonstrates that the proposed model can effectively reduce the total operating expenses of VPPs by facilitating the allocation of electric, thermal, and hydrogen energy and achieving low-carbon emission operations. Full article

(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)

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14 pages, 4942 KiB

Open AccessArticle

Estimation of Route-Choice Behavior Along LRT Lines Using Inverse Reinforcement Learning

by Tomohiro Okubo, Akihiro Kobayashi, Daisuke Kamisaka and Akinori Morimoto

Inventions 2024, 9(6), 118; https://doi.org/10.3390/inventions9060118 - 1 Dec 2024

Viewed by 471

Abstract

As the decline of public transportation in rural areas becomes a growing concern, initiatives to introduce attractive next-generation transportation systems to promote public transportation usage are being considered across various regions. In Toyama City, Toyama Prefecture, where the next-generation light rail transit (LRT) [...] Read more.

As the decline of public transportation in rural areas becomes a growing concern, initiatives to introduce attractive next-generation transportation systems to promote public transportation usage are being considered across various regions. In Toyama City, Toyama Prefecture, where the next-generation light rail transit (LRT) system has been introduced, the number of users has significantly increased compared to before its introduction, with some users riding the LRT for the sake of the experience itself. On the other hand, there is a demand for a more micro-level and quantitative evaluation of the impact that the LRT has on the liveliness of areas along its route. Therefore, this study uses inverse reinforcement learning (IRL), a type of machine learning, to build a model that estimates route-choice behavior along the LRT lines based on behavioral trajectories generated from smartphone location data. The model is capable of evaluating the characteristics of location data with high accuracy. The findings indicate that routes along the LRT lines tend to be selected, suggesting that both the appeal of the LRT itself and the attractiveness of the spaces along its route contribute to this tendency. Full article

(This article belongs to the Special Issue Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems)

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15 pages, 1313 KiB

Open AccessArticle

Distributed Dispatch of Distribution Network Operators, Distributed Energy Resource Aggregators, and Distributed Energy Resources: A Three-Level Conditional Value-at-Risk Optimization Model

by Qifeng Huang, Hanmiao Cheng, Zhong Zhuang, Meimei Duan, Kaijie Fang, Yixuan Huang and Liyu Wang

Inventions 2024, 9(6), 117; https://doi.org/10.3390/inventions9060117 - 25 Nov 2024

Viewed by 436

Abstract

To enhance the participation enthusiasm of distributed energy resources (DERs) and DER aggregators in their demand response, this paper develops a three-level distributed scheduling model for the distribution network operators (DNO), DER aggregators, and DERs based on the conditional value-at-risk (CVaR) theory. First, [...] Read more.

To enhance the participation enthusiasm of distributed energy resources (DERs) and DER aggregators in their demand response, this paper develops a three-level distributed scheduling model for the distribution network operators (DNO), DER aggregators, and DERs based on the conditional value-at-risk (CVaR) theory. First, a demand response model is established for the DNO, DER aggregators, and DERs. Next, we employ the analytical target cascading (ATC) method to construct a three-level distributed scheduling model, where incentive and compensation prices are shared as consensus variables across the model levels to amplify the influence of DER aggregators on incentive prices and DERs on compensation prices. Then, the photovoltaic output model is restructured using the CVaR theory to effectively measure the risk associated with photovoltaic output uncertainty. Finally, an analysis is conducted using the IEEE 33-node distribution network to validate the effectiveness of the proposed model. Full article

(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)

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26 pages, 5341 KiB

Open AccessReview

Systematic Review on Additive Friction Stir Deposition: Materials, Processes, Monitoring and Modelling

by Evren Yasa, Ozgur Poyraz, Anthony Molyneux, Adrian Sharman, Guney Mert Bilgin and James Hughes

Inventions 2024, 9(6), 116; https://doi.org/10.3390/inventions9060116 - 13 Nov 2024

Viewed by 836

Abstract

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology [...] Read more.

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology that utilises bar feedstocks as the input material and frictional–deformational heat as the energy source. AFSD offers high deposition rates and is a promising technique for achieving defect-free material properties like wrought aluminium, magnesium, steel, and titanium alloys. While it offers benefits in terms of productivity and material properties, its low technology readiness level prevents widespread adoption. Academics and engineers are conducting research across various subfields to better understand the process parameters, material properties, process monitoring, and modelling of the AFSD technology. Yet, it is also crucial to compile and compare the research findings from past studies on this new technology to gain a comprehensive understanding and pinpoint future research paths. This paper aims to present a comprehensive review of AFSD focusing on process parameters, material properties, monitoring, and modelling. In addition to examining data from existing studies, this paper identifies areas where research is lacking and suggests paths for future research efforts. Full article

(This article belongs to the Special Issue Revolutionizing Manufacturing: Advances in Additive Manufacturing Technologies)

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21 pages, 3242 KiB

Open AccessArticle

Information and Analytical System Monitoring and Assessment of the Water Bodies State in the Mineral Resources Complex

by Olga Afanaseva, Mikhail Afanasyev, Semyon Neyrus, Dmitry Pervukhin and Dmitry Tukeev

Inventions 2024, 9(6), 115; https://doi.org/10.3390/inventions9060115 - 12 Nov 2024

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Abstract

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources that meet essential quality standards. This challenge is addressed by implementing a series of organizational and technical measures aimed at preserving the ecology of water [...] Read more.

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources that meet essential quality standards. This challenge is addressed by implementing a series of organizational and technical measures aimed at preserving the ecology of water basins and reducing the level of harmful industrial emissions and other pollutants in the aquatic environment. To guarantee the necessary quality of water resources, monitoring is conducted based on selected parameters using various methods and means of technical quality control. From these results, suitable measures are formulated and applied to maintain water quality. Various scientific works extensively discuss different approaches to water quality management and compliance with specified requirements. Modern strategies for developing water monitoring systems leverage the capabilities of information systems that collect, process, store, and transmit information, enabling the resolution of issues in geographically distributed water bodies in real time. This paper proposes an approach that employs mathematical methods to identify the most significant factors determining water quality and to assess their interrelations using methods of a priori ranking, multivariate correlation regression analysis, and integral quantitative assessment. A hardware and software solution for the development of a unified integrated information and analytical system is proposed. This system enables continuous monitoring and assessment of water bodies based on a set of key parameters, addressing a range of critical tasks. This paper provides a detailed description of the software product, presents a demonstration using real-world data, and discusses the anticipated benefits of implementing such an information and analytical system. Full article

(This article belongs to the Special Issue Innovative Monitoring Techniques and Modeling Approaches for Natural Hazards)

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24 pages, 3192 KiB

Open AccessArticle

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

by Amarendra Alluri, Srinivasa Rao Gampa, Balaji Gutta, Mahesh Babu Basam, Kiran Jasthi, Nibir Baran Roy and Debapriya Das

Inventions 2024, 9(6), 114; https://doi.org/10.3390/inventions9060114 - 12 Nov 2024

Viewed by 533

Abstract

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution [...] Read more.

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies. Full article

(This article belongs to the Special Issue Recent Advances and Challenges in Emerging Power Systems: 2nd Edition)

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23 pages, 4732 KiB

Open AccessArticle

Enhancing Real-Time Emotion Recognition in Classroom Environments Using Convolutional Neural Networks: A Step Towards Optical Neural Networks for Advanced Data Processing

by Nuphar Avital, Idan Egel, Ido Weinstock and Dror Malka

Inventions 2024, 9(6), 113; https://doi.org/10.3390/inventions9060113 - 4 Nov 2024

Viewed by 702

Abstract

In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been [...] Read more.

In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices. Full article

(This article belongs to the Special Issue Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems)

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16 pages, 5378 KiB

Open AccessArticle

Results on the Use of an Original Burner for Reducing the Three-Way Catalyst Light-Off Time

by Adrian Clenci, Bogdan Cioc, Julien Berquez, Victor Iorga-Simăn, Robert Stoica and Rodica Niculescu

Inventions 2024, 9(6), 112; https://doi.org/10.3390/inventions9060112 - 29 Oct 2024

Viewed by 721

Abstract

Individual road mobility comes with two major challenges: greenhouse gas emissions related to global warming and chemical pollution. For the pollution reduction in the spark ignition engine vehicle, the standard and reliable aftertreatment technology is the three-way catalytic converter (TWC). However, the TWC [...] Read more.

Individual road mobility comes with two major challenges: greenhouse gas emissions related to global warming and chemical pollution. For the pollution reduction in the spark ignition engine vehicle, the standard and reliable aftertreatment technology is the three-way catalytic converter (TWC). However, the TWC starts to convert once an optimal temperature, usually known as the light-off temperature, is reached. There are many methods to reduce the warm-up period of the TWC, among which is using a burner. The initial question underlying this study was to see if the use of a relatively straightforward extra-combustion device mounted upstream the TWC, without complex elements, was able to serve the purpose of reducing the light-off time. Consequently, an original burner was designed and investigated numerically via the CFD method and experimentally via measurements of the temperature evolution within a TWC, along with the emissions specific to the burner’s operation. The main findings of this study are: (1) the CFD-based examination is a good way to decide on how to achieve the so-called fit-for-purpose internal aerodynamics of the burner (i.e., to obtain a homogeneous mixture) and (2) to reach the light-off temperature, conventionally taken as 500 K, the burner was operated for 5.2 s, i.e., 3.6 g of gasoline injected, 2.7 g of CO₂ and 1.351 g of CO, respectively, emitted. Moreover, this study identified measures for improving the burner’s design as well as an enhanced procedure for the burner’s operating control both aiming to produce a cleaner combustion during the TWC pre-heating. Full article

(This article belongs to the Special Issue Innovative Research and Applications in Hydrodynamics and Flow Control, 2nd Edition)

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Inventions, Volume 9, Issue 6 (December 2024) – 8 articles

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