Energies

18 pages, 3551 KiB

Open AccessArticle

Research on Energy-Saving Transformation of Rural Residential Building Envelope Structures and Heating Modes in Northeast China

by Zhizheng Zhang, Yunfeng Hua, Na Peng and Kailong Liu

Energies 2024, 17(20), 5195; https://doi.org/10.3390/en17205195 (registering DOI) - 18 Oct 2024

Abstract

Rural areas in Northeast China present a large demand for heating energy in winter, but there are problems in such areas with poor thermal performance of building envelopes and poor indoor thermal comfort. In addition, coal-fired boilers are still widely used. China’s “Dual-Carbon [...] Read more.

Rural areas in Northeast China present a large demand for heating energy in winter, but there are problems in such areas with poor thermal performance of building envelopes and poor indoor thermal comfort. In addition, coal-fired boilers are still widely used. China’s “Dual-Carbon Goals” and “Clean Heating” policy call for the creation of a green and comfortable living environment for rural residential buildings. This paper considers the impact of the improvement of the thermal performance of envelope structures on the initial investment of the transformation program and the rated power of the ASHP and proposes an energy-saving transformation method to replace traditional coal-fired boilers with the ASHP on the basis of the improvement of the thermal performance of envelope structures. By establishing a typical rural residential building model in Northeast China, this energy-saving method is simulated based on TRNSYS. The results show that the payback period of investment of the transformation method of “envelope structure + heating system” is not superior to that of the transformation method of only improving the thermal performance of the envelope structure, but it has advantages in the comprehensive life-cycle benefits and it has great advantages in improving the satisfaction of rural residents in the use of heating systems. Full article

(This article belongs to the Section G: Energy and Buildings)

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23 pages, 1564 KiB

Open AccessArticle

The Emissions of a Compression-Ignition Engine Fuelled by a Mixture of Crude Oil and Biodiesel from the Lipids Accumulated in the Waste Glycerol-Fed Culture of Schizochytrium sp.

by Marcin Zieliński, Marcin Dębowski, Joanna Kazimierowicz and Ryszard Michalski

Energies 2024, 17(20), 5193; https://doi.org/10.3390/en17205193 (registering DOI) - 18 Oct 2024

Abstract

Microalgae are considered to be a promising and prospective source of lipids for the production of biocomponents for conventional liquid fuels. The available sources contain a lot of information about the cultivation of biomass and the amounts and composition of the resulting bio-oils. [...] Read more.

Microalgae are considered to be a promising and prospective source of lipids for the production of biocomponents for conventional liquid fuels. The available sources contain a lot of information about the cultivation of biomass and the amounts and composition of the resulting bio-oils. However, there is a lack of reliable and verified data on the impact of fuel blends based on microalgae biodiesel on the quality of the emitted exhaust gas. Therefore, the main objective of the study was to present the emission characteristics of a compression-ignition engine fuelled with a blend of diesel fuel and biodiesel produced from the lipids accumulated in the biomass of a heterotrophic culture of Schizochytrium sp. The final concentrations of microalgal biomass and lipids in the culture were 140.7 ± 13.9 g/L and 58.2 ± 1.1 g/L, respectively. The composition of fatty acids in the lipid fraction was dominated by decosahexaenoic acid (43.8 ± 2.8%) and palmitic acid (40.4 ± 2.8%). All parameters of the bio-oil met the requirements of the EN 14214 standard. It was found that the use of bio-components allowed lower concentrations of hydrocarbons in the exhaust gas, ranging between 33 ± 2 ppm and 38 ± 7 ppm, depending on the load level of the engine. For smoke opacity, lower emissions were found in the range of 50–100% engine load levels, where the observed content was between 23 ± 4% and 53 ± 8%. Full article

(This article belongs to the Special Issue Advances in Analysis and Application of Biofuels, Alternative fuels and Combustion Technology)

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32 pages, 25127 KiB

Open AccessArticle

Envelope Deficiencies and Thermo-Hygrometric Challenges in Warehouse-Type Buildings in Subtropical Climates: A Case Study of a Nori Distribution Center

by Yue Xu, Hiroatsu Fukuda, Xindong Wei and Tingting Yin

Energies 2024, 17(20), 5192; https://doi.org/10.3390/en17205192 (registering DOI) - 18 Oct 2024

Abstract

Enhancing the energy efficiency and climate resilience of existing buildings is crucial amid growing environmental challenges. While extensive research has focused on non-residential buildings, studies on thermo-hygrometric conditions in warehouse-type buildings, particularly in subtropical climates, remain limited. This study investigated the impact of [...] Read more.

Enhancing the energy efficiency and climate resilience of existing buildings is crucial amid growing environmental challenges. While extensive research has focused on non-residential buildings, studies on thermo-hygrometric conditions in warehouse-type buildings, particularly in subtropical climates, remain limited. This study investigated the impact of building envelope deficiencies on indoor thermal and moisture regulation at the Nori Distribution Center. Using infrared thermal imaging and long-term environmental monitoring, significant thermo-hygrometric fluctuations were identified, primarily due to design and construction deficiencies. Poor insulation, inadequate sealing, and the lack of moisture barriers contributed to unstable indoor temperature and humidity. Seasonal analysis showed that during summer, the median second-floor air temperature reached 28.8 °C, peaking at 39.2 °C, with relative humidity exceeding 70% for 45% of the time. First-floor relative humidity surpassed 70% for 72% of the time. While condensation risk remains low year-round, it increases significantly with air infiltration through gaps in the building envelope. This study recommends enhancing the sealing of the building envelope, upgrading insulation materials and moisture barriers, particularly in the roof, and optimizing the HVAC system to improve energy efficiency and storage conditions. These findings offer valuable recommendations for retrofitting warehouse-type buildings in subtropical climates to improve energy efficiency and climate resilience. Full article

(This article belongs to the Special Issue Thermal Behaviour, Energy Efficiency in Buildings and Sustainable Construction: 4th Edition)

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19 pages, 1952 KiB

Open AccessReview

Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production

by Hyeongjun Lim and Sojung Kim

Energies 2024, 17(20), 5191; https://doi.org/10.3390/en17205191 (registering DOI) - 18 Oct 2024

Abstract

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in [...] Read more.

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks. Full article

(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System, Volume II)

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29 pages, 5673 KiB

Open AccessArticle

Functionality of Bearings in the Shafts of a Vertical-Axis Wind Turbine

by Stanisław Szweda, Grzegorz Głuszek, Marek Szyguła, Wojciech Grzegorzek, Daniel Adamecki, Jarosław Mikuła and Stanisław Mikuła

Energies 2024, 17(20), 5189; https://doi.org/10.3390/en17205189 (registering DOI) - 18 Oct 2024

Abstract

The article contains a description of the design solutions proposed by the authors for a hybrid wind turbine bearing, in which the sliding part takes over the load to the turbine shaft after reaching the shaft rotation speed, ensuring hydrodynamic lubrication of the [...] Read more.

The article contains a description of the design solutions proposed by the authors for a hybrid wind turbine bearing, in which the sliding part takes over the load to the turbine shaft after reaching the shaft rotation speed, ensuring hydrodynamic lubrication of the plain bearing and relieving the rolling bearing. This allows for low starting resistance of the power plant and ensures quiet operation during use. Two conceptual solutions of a hybrid bearing were presented, differing in the shape of the plain bearing journal. A mechanism for automatic switching of the load between a rolling and a plain bearing was developed. A solid simulation model of this mechanism was built in the Autodesk Inventor—Dynamic Simulation software Inventor Professional 2023 environment, and its operation was simulated. The results confirmed the usefulness of using this design in shaft-bearing systems of wind turbines with a vertical axis of rotation. Based on the simulation, the speed at which the thrust roller bearing will be released was determined. Technical parameters of a plain bearing with a spherical journal shape were calculated. The height of the lubrication gap and the shaft rotational speed at which the bearing load capacity index reaches a critical value were determined. Full article

(This article belongs to the Special Issue Energy Security and Just Transition)

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20 pages, 4568 KiB

Open AccessArticle

Neutronics Analysis on High-Temperature Gas-Cooled Pebble Bed Reactors by Coupling Monte Carlo Method and Discrete Element Method

by Kashminder S. Mehta, Braden Goddard and Zeyun Wu

Energies 2024, 17(20), 5188; https://doi.org/10.3390/en17205188 (registering DOI) - 18 Oct 2024

Abstract

The High-Temperature Gas-Cooled Pebble Bed Reactor (HTG-PBR) is notable in the advanced reactor realm for its online refueling capabilities and inherent safety features. However, the multiphysics coupling nature of HTG-PBR, involving neutronic analysis, pebble flow movement, and thermo-fluid dynamics, creates significant challenges for [...] Read more.

The High-Temperature Gas-Cooled Pebble Bed Reactor (HTG-PBR) is notable in the advanced reactor realm for its online refueling capabilities and inherent safety features. However, the multiphysics coupling nature of HTG-PBR, involving neutronic analysis, pebble flow movement, and thermo-fluid dynamics, creates significant challenges for its development, optimization, and safety analysis. This study focuses on the high-fidelity neutronic modelling and analysis of HTG-PBR with an emphasis on achieving an equilibrium state of the reactor for long-term operations. Computational approaches are developed to perform high-fidelity neutronics analysis by coupling the superior modelling capacities of the Monte Carlo Method (MCM) and Discrete Element Method (DEM). The MCM-based code OpenMC and the DEM-based code LIGGGHTS are employed to simulate the neutron transport and pebble movement phenomena in the reactor, respectively. To improve the computational efficiency to expedite the equilibrium core search process, the reactor core is discretized by grouping pebbles in axial and radial directions with the incorporation of the pebble position information from DEM simulations. The OpenMC model is modified to integrate fuel circulation and fresh fuel loading. All of these measures ultimately contribute to a successful generation of an equilibrium core for HTG-PBR. For demonstration, X-energy’s Xe-100 reactor—a 165 MW thermal power HTG-PBR—is used as the model reactor in this study. Starting with a reactor core loaded with all fresh pebbles, the equilibrium core search process indicates the continuous loading of fresh fuel is required to sustain the reactor operation after 1000 days of fuel depletion with depleted fuel circulation. Additionally, the model predicts 213 fresh pebbles are needed to add to the top layer of the reactor to ensure the k_eff does not reduce below the assumed reactivity limit of 1.01. Full article

(This article belongs to the Special Issue Advanced Multi-physics Modelling and Simulation for Nuclear Technology)

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18 pages, 8617 KiB

Open AccessArticle

A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling

by Nastaran Gholizadeh and Petr Musilek

Energies 2024, 17(20), 5187; https://doi.org/10.3390/en17205187 (registering DOI) - 18 Oct 2024

Abstract

Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven [...] Read more.

Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable. Full article

(This article belongs to the Special Issue Digitization, Flexibility and Energy Storage in Power Generation Systems Employing Renewable Energy)

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15 pages, 1018 KiB

Open AccessArticle

Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems

by Quota Alief Sias, Rahma Gantassi, Yonghoon Choi and Jeong Hwan Bae

Energies 2024, 17(20), 5186; https://doi.org/10.3390/en17205186 (registering DOI) - 18 Oct 2024

Abstract

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which [...] Read more.

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period. Full article

(This article belongs to the Section F: Electrical Engineering)

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12 pages, 3098 KiB

Open AccessArticle

Effect of Potassium on the Co-Combustion Process of Coal Slime and Corn Stover

by Jing Zheng, Chuanchuan Cai, Tao Ge and Mingxu Zhang

Energies 2024, 17(20), 5185; https://doi.org/10.3390/en17205185 (registering DOI) - 18 Oct 2024

Abstract

In this study, the combined combustion characteristics and gaseous product emissions of coal slime and corn stover were compared at different blending ratios. The TG-DTG curves indicate that the optimal performance is achieved when the corn straw blending ratio is 20%. Furthermore, the [...] Read more.

In this study, the combined combustion characteristics and gaseous product emissions of coal slime and corn stover were compared at different blending ratios. The TG-DTG curves indicate that the optimal performance is achieved when the corn straw blending ratio is 20%. Furthermore, the TG-FTIR coupling results demonstrated an increase in gas species as the blending ratio increased. The composition analysis of ash samples formed at various combustion temperatures using XRD and XRF indicated that a portion of KCl in the fuel was released as volatile matter, while another part reacted with Al₂O₃ and SiO₂ components in the slime to form silica–aluminate compounds and other substances. Notably, interactions between the components of slime and potassium elements in corn stover primarily occurred within the temperature range of 800–1000 °C. These findings contribute to a comprehensive understanding of biomass and coal co-firing combustion chemistry, offering potential applications for enhancing energy efficiency and reducing emissions in industrial processes. Full article

(This article belongs to the Section I2: Energy and Combustion Science)

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16 pages, 1469 KiB

Open AccessReview

Innovations for Holistic and Sustainable Transitions

by Phoebe Koundouri, Angelos Alamanos, Stathis Devves, Conrad Landis and Kostantinos Dellis

Energies 2024, 17(20), 5184; https://doi.org/10.3390/en17205184 (registering DOI) - 18 Oct 2024

Abstract

Energy system planning has evolved from a narrow focus on engineering and supply works towards addressing more complex, multifactorial challenges. Increasingly challenged by climate change, extreme events, economic shocks, and altered supply demand patterns, the analysis of energy systems requires holistic approaches based [...] Read more.

Energy system planning has evolved from a narrow focus on engineering and supply works towards addressing more complex, multifactorial challenges. Increasingly challenged by climate change, extreme events, economic shocks, and altered supply demand patterns, the analysis of energy systems requires holistic approaches based on data-driven models, taking into account key socio-economic factors. We draw insights from reviewing the literature, indicating the need to cover the following major gaps: the shift to transdisciplinary approaches, incorporating environmental system analysis; resilient and sustainable energy designs based on flexible portfolios of renewable mixes; the integration of socio-economic aspects, economic analyses and behavioural models to ensure energy systems are not only technically sound but socially acceptable and viable; the need for stakeholder engagement considering the human angle in energy security and behavioural shifts. Responding to these pressing challenges and emerging needs, the Global Climate Hub (GCH) initiative, operating under the UN Sustainable Development Solutions Network, offers a conceptual framework, leveraging transdisciplinary approaches. In this Concept Paper, we present for the first time the idea of the GCH as a framework that we believe has the potential to address the modern holistic needs for energy system analysis and policymaking. By setting the conceptual/theoretical ground of our suggested approach, we aim to provide guidance for innovative combinations of cutting-edge models, socio-economic narratives, and inclusive interaction with relevant stakeholders for the development and the long-term implementation of sustainable pathways. Full article

(This article belongs to the Special Issue Energy and Environmental Economic Theory and Policy)

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14 pages, 2216 KiB

Open AccessArticle

Autoencoder-Driven Training Data Selection Based on Hidden Features for Improved Accuracy of ANN Short-Term Load Forecasting in ADMS

by Zoran Pajić, Zoran Janković and Aleksandar Selakov

Energies 2024, 17(20), 5183; https://doi.org/10.3390/en17205183 (registering DOI) - 17 Oct 2024

Abstract

This paper presents a novel methodology for short-term load forecasting in the context of significant shifts in the daily load curve due to the rapid and extensive adoption of Distributed Energy Resources (DERs). The proposed solution, built upon the Similar Days Method (SDM) [...] Read more.

This paper presents a novel methodology for short-term load forecasting in the context of significant shifts in the daily load curve due to the rapid and extensive adoption of Distributed Energy Resources (DERs). The proposed solution, built upon the Similar Days Method (SDM) and Artificial Neural Network (ANN), introduces several novelties: (1) selection of similar days based on hidden representations of day data using Autoencoder (AE); (2) enhancement of model generalization by utilizing a broader set of training examples; (3) incorporating the relative importance of training examples derived from the similarity measure during training; and (4) mitigation of the influence of outliers by applying an ensemble of ANN models trained with different data splits. The presented AE configuration and procedure for selecting similar days generated a higher-quality training dataset, which led to more robust predictions by the ANN model for days with unexpected deviations. Experiments were conducted on actual load data from a Serbian electrical power system, and the results were compared to predictions obtained by the field-proven STLF tool. The experiments demonstrated an improved performance of the presented solution on test days when the existing STLF tool had poor predictions over the past year. Full article

(This article belongs to the Special Issue Advances in Power and Energy Management for Distribution Systems with High Penetration of Distributed Energy Resources)

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14 pages, 15081 KiB

Open AccessArticle

Implementation of Regenerative Thermal Oxidation Device Based on High-Heating Device for Low-Emission Combustion

by Hoon-Min Park, Dal-Hwan Yoon, Joon-Seong Lee, Hyun-Min Jung, Dae-Hee Lee, Dong-Hwan Jeon and Tae-Yeung Lim

Energies 2024, 17(20), 5182; https://doi.org/10.3390/en17205182 (registering DOI) - 17 Oct 2024

Abstract

In this paper, a heating device is implemented by considering two large factors in a 100 cmm RTO design. First, when the combustion chamber is used for a long time with a high temperature of 750–1100 °C depending on the high concentration VOC [...] Read more.

In this paper, a heating device is implemented by considering two large factors in a 100 cmm RTO design. First, when the combustion chamber is used for a long time with a high temperature of 750–1100 °C depending on the high concentration VOC gas capacity, there is a problem that the combustion chamber explodes or the function of the rotary is stopped due to the fatigue and load of the device. To prevent this, the 100 cmm RTO design with a changed rotary position is improved. Second, an RTO design with a high-heating element is implemented to combust VOC gas discharged from the duct at a stable temperature. Through this, low-emission combustion emissions and energy consumption are reduced. By implementing a high heat generation device, the heat storage combustion oxidation function is improved through the preservation of renewable heat. Over 177 h of demonstration time, we improved the function of 100 cm by discharging 99% of VOC’s removal efficiency, 95.78% of waste heat recovery rate, 21.95% of fuel consumption, and 3.9 ppm of nitrogen oxide concentration. Full article

(This article belongs to the Special Issue Advances in Green Energy, Environment and Carbon Neutralization 2022–2024)

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16 pages, 633 KiB

Open AccessArticle

Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer

by Mingxiang Li, Tianyi Zhang, Haizhu Yang and Kun Liu

Energies 2024, 17(20), 5181; https://doi.org/10.3390/en17205181 - 17 Oct 2024

Abstract

In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting [...] Read more.

In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction. Full article

(This article belongs to the Special Issue Advancements in the Integrated Energy System and Its Policy)

16 pages, 1509 KiB

Open AccessArticle

The Consequences of Economy Servitization for Ensuring Energy Sustainability—The Case of Developed and Developing Countries

by Mateusz Jankiewicz and Elżbieta Szulc

Energies 2024, 17(20), 5180; https://doi.org/10.3390/en17205180 - 17 Oct 2024

Abstract

This study presents an analysis of the relationship between the servitization process and energy sustainability in the years 2015–2020. The research refers to 164 selected countries, also divided into two regimes: developed and developing. The transformation of the manufacturing process, and as a [...] Read more.

This study presents an analysis of the relationship between the servitization process and energy sustainability in the years 2015–2020. The research refers to 164 selected countries, also divided into two regimes: developed and developing. The transformation of the manufacturing process, and as a result, the economy’s structure, towards servitization, is observed in most countries worldwide. The positive influence of the servitization of production by individual manufacturers on sustainability is widely known. In this research, this relationship is considered on a macroeconomic scale, which is one of the novelties of the study. Particularly, sustainability in the energy sector, indicated as an achievement of the 7th goal of Sustainable Development, is discussed. Energy sustainability is evaluated using a synthetic measure by Perkal. This part of the research shows the problem of the low level of energy sustainability in developing countries (particularly in Africa) compared with developed ones. Moreover, spatio-temporal sensitivity models are estimated and verified. The sensitivity parameter in these models shows the impact of the progress in the servitization process on energy sustainability. The models have been enriched with the effects of spatial dependence between countries, taking into account two types of proximity matrices based on (1) the common border criterion and (2) the similarity of the development levels measured by the Human Development Index. Additionally, the differences in sensitivity between developed and developing countries are considered. The results of the study show that in both cases, the economic servitization positively influences energy sustainability, but the strength of the relationship is stronger in the group of developed countries. This can be, for example, the result of the individual characteristics of the given countries, where African countries mainly benefit from agricultural development. Only after reaching a certain level of economic growth will they be able to obtain sustainability faster through economic servitization. Full article

(This article belongs to the Section C: Energy Economics and Policy)

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32 pages, 7362 KiB

Open AccessArticle

Evaluating and Prioritizing Circular Supply Chain Alternatives in the Energy Context with a Holistic Multi-Indicator Decision Support System

by Thanh Quang Nguyen, Sonia Longo, Maurizio Cellura, Le Quyen Luu, Alessandra Bertoli and Letizia Bua

Energies 2024, 17(20), 5179; https://doi.org/10.3390/en17205179 - 17 Oct 2024

Abstract

Transitioning to a circular economy is crucial for sustainable energy development; yet, current energy supply chains lack comprehensive assessment tools. This study introduces the Holistic Multi-Indicator Decision Support System (HMI_DSS), an innovative tool grounded in life cycle thinking and advanced multi-criteria decision-making methodologies, [...] Read more.

Transitioning to a circular economy is crucial for sustainable energy development; yet, current energy supply chains lack comprehensive assessment tools. This study introduces the Holistic Multi-Indicator Decision Support System (HMI_DSS), an innovative tool grounded in life cycle thinking and advanced multi-criteria decision-making methodologies, including Entropy and PROMETHEE II. The HMI_DSS quantifies and assesses sustainability and circularity in energy systems by employing 49 indicators, with a focus on energy efficiency and greenhouse gas emissions. A case study on the rice straw energy supply chain for biogas production illustrates the tool’s effectiveness, comparing a baseline scenario to an alternative. The results show that the global warming potential (GWP) of the baseline is 122 gCO₂eq/kWh, while the alternative is 116 gCO₂eq/kWh. However, the baseline scenario has lower energy consumption (1.72 × 10⁷ MJ annually) than the alternative (1.98 × 10⁷ MJ). Overall, the alternative outperforms the baseline in terms of sustainability and circularity. The HMI_DSS offers a flexible and robust framework for evaluating trade-offs in energy systems, providing valuable insights for energy companies and researchers in adopting circular economy principles to achieve sustainable development. Full article

(This article belongs to the Section A: Sustainable Energy)

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29 pages, 6734 KiB

Open AccessArticle

Dynamic Modeling of Distribution Power Systems with Renewable Generation for Stability Analysis

by Darko Madjovski, Ivan Dumancic and Carolina Tranchita

Energies 2024, 17(20), 5178; https://doi.org/10.3390/en17205178 - 17 Oct 2024

Abstract

This paper presents a comprehensive study on the dynamic modeling of distribution power systems with a focus on the integration of renewable energy sources (RESs) for stability analysis. Our research delves into the static and dynamic behavior of distribution systems, emphasizing the need [...] Read more.

This paper presents a comprehensive study on the dynamic modeling of distribution power systems with a focus on the integration of renewable energy sources (RESs) for stability analysis. Our research delves into the static and dynamic behavior of distribution systems, emphasizing the need for enhanced load modeling to mitigate planning and operational uncertainties. Using MATLAB/Simulink^®, we simulate four distinct study cases characterized by varying load types and levels of distributed generation (DG), particularly solar PV, under both balanced and unbalanced conditions. Our findings highlight the critical role of DG in influencing voltage stability, revealing that deviations in voltage and current during grid imbalances remain within acceptable limits. The study underscores the importance of DG-based inverters in maintaining grid stability through reactive power support and sets the stage for future research on microgrid simulations and battery storage integration to further enhance system stability and performance. Full article

(This article belongs to the Special Issue Advanced Control, Optimization, Stability and Reliability of Microgrids and Power Systems)

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38 pages, 3027 KiB

Open AccessReview

A Survey of Computer Vision Detection, Visual SLAM Algorithms, and Their Applications in Energy-Efficient Autonomous Systems

by Lu Chen, Gun Li, Weisi Xie, Jie Tan, Yang Li, Junfeng Pu, Lizhu Chen, Decheng Gan and Weimin Shi

Energies 2024, 17(20), 5177; https://doi.org/10.3390/en17205177 - 17 Oct 2024

Abstract

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer [...] Read more.

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends. Full article

(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)

23 pages, 5670 KiB

Open AccessArticle

Comparative Analysis and Integrated Methodology for the Electrical Design and Performance Evaluation of Thermoelectric Generators (TEGs) in Energy Harvesting Applications

by Oswaldo Hideo Ando Junior, Eder Andrade da Silva, Emerson Rodrigues de Lira, Sergio Vladimir Barreiro Degiorgi and João Paulo Pereira do Carmo

Energies 2024, 17(20), 5176; https://doi.org/10.3390/en17205176 - 17 Oct 2024

Abstract

This study presents a comparative analysis of the accuracy of different methodologies for the design and performance evaluation of thermoelectric generators (TEGs), using analytical, computational numerical, and experimental approaches. TEGs are promising devices for capturing waste energy in industrial processes, converting waste heat [...] Read more.

This study presents a comparative analysis of the accuracy of different methodologies for the design and performance evaluation of thermoelectric generators (TEGs), using analytical, computational numerical, and experimental approaches. TEGs are promising devices for capturing waste energy in industrial processes, converting waste heat into electrical energy and contributing to energy sustainability. However, the efficiency of TEGs is a significant challenge due to their low conversion rates. To address this challenge, three different methodologies were developed and systematically compared. Analytical Model: Developed for the electrical design of thermoelectric micro generators, using theoretical performance data and industrial temperature gradients. This method offers a robust theoretical view but may not capture all practical variables. Computational model in Simulink/MATLAB: Created and validated to consider the variation of the Seebeck coefficient and the internal resistance of thermoelectric modules with temperature. This model provides an accurate simulation of operating conditions but depends on the accuracy of the input parameters. Experimental Multi-string Electrical Arrangement Prototype: This involved the design and construction of a prototype followed by experimental tests to validate its performance. This method provides valuable empirical data but can be limited by the complexity and cost of the experiments. The results show that each methodology has specific advantages and limitations, offering valuable insights for the development of more efficient TEG systems. The comparison of analytical, numerical, and experimental methods revealed differences in accuracy and efficiency, highlighting the importance of an integrated approach to TEG design. This study lays a solid foundation for future research and practical applications in the field of industrial residual energy harvesting. Full article

(This article belongs to the Special Issue Distributed Energy Resources: Advances, Challenges and Future Trends)

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23 pages, 3240 KiB

Open AccessArticle

Development of the Separation Column’s Temperature Field Monitoring System

by Tatyana Kukharova, Alexander Martirosyan, Mir-Amal Asadulagi and Yury Ilyushin

Energies 2024, 17(20), 5175; https://doi.org/10.3390/en17205175 - 17 Oct 2024

Abstract

Oil is one of the main resources used by all countries in the world. The ever-growing demand for oil and oil products forces oil companies to increase production and refining. In order to increase net profit, oil producing companies are constantly upgrading equipment, [...] Read more.

Oil is one of the main resources used by all countries in the world. The ever-growing demand for oil and oil products forces oil companies to increase production and refining. In order to increase net profit, oil producing companies are constantly upgrading equipment, improving oil production technologies, and preparing oil for further processing. When considering the elements of primary oil refining in difficult conditions, such as hard-to-reach or in remote locations, developers face strict limitations in energy resources and dimensions. Therefore, the use of traditional systems causes a number of difficulties, significantly reducing production efficiency. In this study, the authors solve the problem of improving the characteristics of the oil separation process. In their work, the authors analyzed the separation columns of primary oil distillation, identified the shortcomings of the technological process, and searched for technological solutions. Having identified the lack of technical solutions for monitoring the state of the temperature field of the separation column, the authors developed their own hardware–software complex for monitoring the separation column (RF patents No. 2020665473, No. 2021662752 were received). The complex was tested and successfully implemented into production. The study provides an assessment of the economic efficiency of implementation for a year and a forecast of the economic effect for 10 years. Full article

(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)

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